Three-dimensional object reconstruction from a video

ABSTRACT

A three-dimensional (3D) object reconstruction neural network system learns to predict a 3D shape representation of an object from a video that includes the object. The 3D reconstruction technique may be used for content creation, such as generation of 3D characters for games, movies, and 3D printing. When 3D characters are generated from video, the content may also include motion of the character, as predicted based on the video. The 3D object construction technique exploits temporal consistency to reconstruct a dynamic 3D representation of the object from an unlabeled video. Specifically, an object in a video has a consistent shape and consistent texture across multiple frames. Texture, base shape, and part correspondence invariance constraints may be applied to fine-tune the neural network system. The reconstruction technique generalizes well—particularly for non-rigid objects.

CLAIM OF PRIORITY

This application is a continuation of U.S. patent application Ser. No.16/945,455 titled “Three-Dimensional Object Reconstruction from aVideo,” filed Jul. 31, 2020, the entire contents of both applicationsare incorporated herein by reference.

TECHNICAL FIELD

The present invention relates to three-dimensional (3D) objectreconstruction, and in particular, to a technique for constructing a 3Dobject from a video.

BACKGROUND

When we humans try to understand an image of an object, such as a duck,we instantly recognize a “duck”. We also instantly perceive and imaginethe shape of the duck in the 3D world the duck's appearance from otherviewpoints. Furthermore, when we see the duck in a video, its 3Dstructure and deformation become even more apparent to us. Our abilityto perceive the 3D structure of objects contributes vitally to our richunderstanding of them.

While 3D perception is easy for humans, 3D reconstruction of deformableobjects remains a very challenging problem in computer vision,especially for objects in the wild. For learning-based algorithms, abottleneck is the lack of supervision available for training. It ischallenging to collect 3D annotations, such as 3D shape and camera pose,without limiting the domain (e.g., rigid objects, human bodies, andfaces) for which 3D annotations can be captured in constrainedenvironments. However, conventional approaches in the limited domains donot generalize well to non-rigid objects captured in naturalisticenvironments (e.g., animals). Due to constrained environments andlimited annotations, it is very difficult to generalize the conventionalapproaches to the 3D construction of non-rigid objects (e.g., animals)from images and videos captured in the wild. There is a need foraddressing these issues and/or other issues associated with the priorart.

SUMMARY

A 3D object reconstruction neural network system learns to predict a 3Drepresentation of an object from a video that includes the object. Anobject in a video maintains temporal consistency, having a consistentshape and consistent texture across multiple frames. The temporalconsistency of the object is exploited to reconstruct a dynamic 3Drepresentation of the object from an unlabeled video. Texture, identityshape, and part correspondence invariance constraints may be applied tofine-tune the neural network system. The reconstruction techniquegeneralizes well, particularly for non-rigid objects and the neuralnetwork system can inference in real time.

A method, computer readable medium, and system are disclosed forconstructing a 3D representation of an object from a video. In anembodiment, a neural network model receives a video including images ofthe object captured from a camera pose and predicts a 3D shaperepresentation of the object for a first image of the images based on aset of learned shape bases. The neural network model also predicts atexture flow for the first image and maps pixels from the first image toa texture space according to the texture flow to produce a textureimage, where transfer of the texture image onto the 3D shaperepresentation constructs a 3D object corresponding to the object in thefirst image.

BRIEF DESCRIPTION OF THE DRAWINGS

The present systems and methods for three-dimensional (3D) objectreconstruction from a video are described in detail below with referenceto the attached drawing figures, wherein:

FIG. 1A illustrates a block diagram of an example 3D objectreconstruction system suitable for use in implementing some embodimentsof the present disclosure.

FIG. 1B illustrates flowchart of a method for reconstructing a 3Drepresentation of an object using the system shown in FIG. 1A suitablefor use in implementing some embodiments of the present disclosure.

FIG. 1C illustrates a conceptual diagram of temporal consistencyconstraints, in accordance with an embodiment.

FIG. 1D illustrates a flowchart of a method for applying self-supervisedadaptation to the system shown in FIG. 1A suitable for use inimplementing some embodiments of the present disclosure.

FIG. 2A illustrates a block diagram of an example training configurationfor the 3D object construction system shown in FIG. 1A suitable for usein implementing some embodiments of the present disclosure.

FIG. 2B illustrates a conceptual diagram of using temporal invariance toencourage parts correspondence, in accordance with an embodiment.

FIG. 2C illustrates a conceptual diagram of training using annotationre-projection suitable for use in implementing some embodiments of thepresent disclosure.

FIG. 2D illustrates a flowchart of a method for training the 3D objectconstruction system shown in FIG. 1A suitable for use in implementingsome embodiments of the present disclosure.

FIG. 3 illustrates images and reconstructed objects, in accordance withan embodiment.

FIG. 4 illustrates an example parallel processing unit suitable for usein implementing some embodiments of the present disclosure.

FIG. 5A is a conceptual diagram of a processing system implemented usingthe PPU of FIG. 4 suitable for use in implementing some embodiments ofthe present disclosure.

FIG. 5B illustrates an exemplary system in which the variousarchitecture and/or functionality of the various previous embodimentsmay be implemented.

FIG. 6A is a conceptual diagram of a graphics processing pipelineimplemented by the PPU of FIG. 4 suitable for use in implementing someembodiments of the present disclosure.

FIG. 6B illustrates an exemplary game streaming system suitable for usein implementing some embodiments of the present disclosure.

DETAILED DESCRIPTION

The task of 3D reconstruction entails the simultaneous recovery of the3D shape, texture, and camera pose of objects from 2D images. The taskis highly ill-posed due to the inherent ambiguity of correctlyestimating both the shape and camera pose together. However, a 3D objectconstruction neural network system may learn to predict 3Drepresentations of the object from video.

In an embodiment, the 3D object construction neural network system istrained to reconstruct temporally consistent 3D meshes of deformableobject instances from videos. In an embodiment, the videos include realanimals in natural environments. Prior to inferencing, the neuralnetwork system is trained to jointly predict the shape, texture, andcamera pose of an image for category-specific 3D reconstruction using acollection of single-view images of the same category. A first examplecategory may include, but is not limited to, birds (including ducks). Asecond example category may be horses. In general, a category includesanimals that have a similar structure, such as animals within a singlespecies. The neural network may be trained without requiring anannotated 3D mesh, 2D keypoints, or camera pose for each video frame.

Then, at inference time, the neural network system is adapted over timeusing self-supervised regularization terms that exploit temporalconsistency of an object instance to enforce that all reconstructedmeshes for the object share a common texture map, a base (identity)shape, as well as parts. As a result of the adaptive refinement, theneural network system recovers temporally consistent and reliable 3Dstructures from videos of non-rigid objects including those of animalscaptured in the wild—a challenging task that is rarely addressed.

FIG. 1A illustrates a block diagram of a 3D object construction system100, in accordance with an embodiment. It should be understood that thisand other arrangements described herein are set forth only as examples.Other arrangements and elements (e.g., machines, interfaces, functions,orders, groupings of functions, etc.) may be used in addition to orinstead of those shown, and some elements may be omitted altogether.Further, many of the elements described herein are functional entitiesthat may be implemented as discrete or distributed components or inconjunction with other components, and in any suitable combination andlocation. Various functions described herein as being performed byentities may be carried out by hardware, firmware, and/or software. Forinstance, various functions may be carried out by a processor executinginstructions stored in memory. Furthermore, persons of ordinary skill inthe art will understand that any system that performs the operations ofthe 3D object construction system 100 is within the scope and spirit ofembodiments of the present invention.

The 3D object construction system 100 includes a neural network modelcomprising at least an encoder 105, shape decoder 115, and motiondecoder 120. The encoder 105 extracts features 110 from each frame(e.g., image) in the video. An input image 102 including an object and apredicted 3D object 104, output by the 3D object construction system100, are illustrated in FIG. 1A. The features 110 are then processed bya variety of decoders to predict an identity shape, motion (offsets),texture, and camera (not shown). In an embodiment, the shape decoder 115outputs an identity shape 116 that represents basic shapes of the samecategory (e.g., duck, flying bird, fat bird, standing bird, etc.). In anembodiment, the identity shape 116 is defined by a 3D mesh of verticesthat define faces of the mesh surface. In an embodiment, the meshes fora particular category are deformed from a predefined sphere and have thesame number of vertices/faces. The motion decoder 120 predicts offsets,∇V relative to each vertex in the identity shape 116. For each videoframe, the offsets define shape deformations that are applied to theidentity shape 116 and, over time, appear as movement.

In contrast with conventional 3D reconstruction techniques, thepredicted shapes are not limited to those that are symmetric. Anassumption of symmetry does not hold for most non-rigid animals, e.g.,birds tilting their heads, or walking horses, etc. This is particularlyimportant for recovering dynamic meshes in sequences, e.g., when a birdrotates its head, the 3D shape is no longer mirror symmetric. Therefore,the assumption of symmetry may be removed and the constructed meshes areallowed to fit more complex, non-rigid poses. Simply removing thesymmetry assumption for the predicted vertex offsets leads to excessivefreedom in shape deformation. To resolve undesirable deformations, a setof N_(b) shape bases {V_(i)}_(i=1) ^(N) ^(b) is learned by the shapedecoder 115 based on the features 110. The base or identity shape 116 iscomputed as a weighted combination of the shape bases, denoted as thebase shape V_(base). Compared to a single mesh template, the base shapeV_(base) is more powerful in capturing the object's identity andrelieves the shape decoder 115 from predicting large motion deformation,e.g., of deforming a standing bird template to a flying bird.

Different sets of shape bases are learned during training by clusteringconstructed meshes, where the meshes in each set share a similar shapeand the base shape is their mean shape. Learned coefficients{β_(i)}_(i=1) ^(N) ^(b) are predicted by the shape decoder 115 and usedto combine corresponding meshes in the set of shape bases to produce theidentity shape 116. The identity shape 116 may be computed as:

$\begin{matrix}{V_{base} = {\sum_{i = 1}^{N_{b}}{\beta_{i}{V_{i}.}}}} & {{Eq}.\mspace{14mu}(1)}\end{matrix}$

The motion decoder 120 predicts offsets ∇V relative to each vertex inthe identity shape 116. The offsets 118 encode the object's asymmetricnon-rigid motion, defining deformations for each vertex in the identityshape 116.

$\begin{matrix}{{V = {V_{base} + {\nabla V}}}.} & {{Eq}.\mspace{14mu}(2)}\end{matrix}$

The offsets 118 are applied to the vertices of the identity shape 116 by3D mesh construction unit 112 to construct a predicted 3D shaperepresentation 108 (e.g., wireframe or mesh) of the object. Thepredicted 3D shape representation 108 is shown as a checkerboard in FIG.1A for visualization purposes, where different faces defined by thevertices in the mesh are colored either black or white.

The texture decoder 125 receives the features 110 and predicts a textureimage 106 for each frame of the video. The texture decoder 125 predictsa texture flow I_(flow)∈

^(H) ^(uv) ^(×W) ^(uv) ^(×2) for each image based on the features 110.The texture flow maps pixels from the image to UV texture space toproduce a texture image 106. A predefined UV mapping function may thenbe used by 3D mesh texture unit 122 to map the texture image 106 fromthe texture space to the 3D shape representation 108. Applying thetexture image 106 to the 3D shape representation 108 produces a 3Dobject 104 (e.g., textured mesh). The 3D object 104 is represented with|V| vertices (V∈

^(|V|×3)), |F| faces (F∈

^(|F|×3)), and the UV texture image I_(uv)∈

^(H) ^(uv) ^(×W) ^(uv) ^(×3) of height H_(uv) and width W_(uv). The UVtexture space provides a parameterization that is invariant to objectdeformation. Therefore, over time, the image texture for the images inthe video should be constant or invariant to shape deformation.

By enforcing the predicted values for the texture images 106 to beconsistent in the UV texture space across different frames of a video,the neural network model can be regularized to generate coherentreconstructions over time during inferencing. The temporal invariancemay be used as self-supervised signals to tune the neural network model.In an embodiment, during inferencing, the 3D object construction system100 is adapted using self-supervised regularization based on shapeinvariance and texture invariance. Specifically, an object in a videomaintains a consistent shape and consistent texture across multipleframes. The self-supervised adaptation technique exploits the temporalconsistency of the object to construct the dynamic 3D object from anunlabeled video. As described further herein, texture image, identityshape, and part correspondence invariance constraints may be applied totrain and/or fine-tune the neural network model.

More illustrative information will now be set forth regarding variousoptional architectures and features with which the foregoing frameworkmay be implemented, per the desires of the user. It should be stronglynoted that the following information is set forth for illustrativepurposes and should not be construed as limiting in any manner. Any ofthe following features may be optionally incorporated with or withoutthe exclusion of other features described.

FIG. 1B illustrates flowchart of a method 130 for constructing a 3Drepresentation of an object using the 3D object reconstruction system100 shown in FIG. 1A, in accordance with an embodiment. Each block ofmethod 130, described herein, comprises a computing process that may beperformed using any combination of hardware, firmware, and/or software.For instance, various functions may be carried out by a processorexecuting instructions stored in memory. The method 130 may also beembodied as computer-usable instructions stored on computer storagemedia. The method 130 may be provided by a standalone application, aservice or hosted service (standalone or in combination with anotherhosted service), or a plug-in to another product, to name a few. Inaddition, method 130 is described, by way of example, with respect tothe system of FIG. 1A. However, this method may additionally oralternatively be executed by any one system, or any combination ofsystems, including, but not limited to, those described herein.Furthermore, persons of ordinary skill in the art will understand thatany system that performs method 130 is within the scope and spirit ofembodiments of the present invention.

At step 135, the 3D object construction system 100 receives a videoincluding images of the object captured from a camera pose. In anembodiment, the video is unlabeled. In an embodiment, the object is anon-rigid animal. In an embodiment, the video is captured in the “wild”.

At step 140, the 3D object construction system 100 predicts the 3D shaperepresentation of the object for a first image of the images based on aset of learned shape bases. In an embodiment, the 3D object constructionsystem 100 predicts an identity shape from a set of learned shapes. Inan embodiment, the identity shape is computed using Equation (1). In anembodiment, the identity shape is computed as a sum of component shapesincluded in the set of learned shape bases and each component shape iscorresponding scaled by a coefficient generated by the neural networkmodel. In an embodiment, shape offsets (e.g., non-rigid motiondeformations) are computed and applied to the vertices of the identityshape to predict the 3D shape representation of the object. In anembodiment, the 3D shape representation is a mesh of vertices thatdefine faces.

At step 145, the 3D object construction system 100 predicts a textureflow for the first image. At step 147, the 3D object construction system100 maps pixels from the first image to a texture space according to thetexture flow to produce a texture image corresponding to the firstimage. In an embodiment, for a particular image, the texture image istransferred onto the 3D shape representation to produce the 3D objectcorresponding to the object in the first image. In an embodiment, steps140, 145, and 147 are repeated for each image in the video. In anembodiment, the 3D construction system 100 also predicts a camera posebased on features extracted from the video. When the 3D object isrendered according to the camera pose for each frame of video, therendered object appears as the object in the frame.

FIG. 1C illustrates a conceptual diagram of temporal consistencyconstraints, in accordance with an embodiment. A neural network model150 comprises the encoder 105, shape decoder 115, and texture decoder125. In an embodiment, to enforce texture invariance, the reconstructedtexture images for an arbitrary pair of frames (e.g., images) in a videosequence are swapped. As shown in FIG. 1C, frames 132 and 136 areprocessed by the neural network model 150 to predict the respectiveidentity shapes 142 and 146. The identity shapes 142 and 146 shown inFIG. 1C are each posed according to a respective camera pose, θ of therespective frames, where θ∈

⁷. In an embodiment, the camera pose represents a perspectivetransformation. Except for the different camera poses, the identityshapes 142 and 146 are the same. The offsets predicted by the neuralnetwork model 150 for the frames 132 and 136 are applied to therespective identity shapes 142 and 146, to produce 3D shaperepresentations 152 and 156.

The shape model is represented by an identity or base shape V_(base) andan offset or deformation term ΔV, in which the identity shape V_(base)intuitively corresponds to the “identity” of the instance, e.g., a duck,or a flying bird, etc. During online adaptation, the neural networkmodel 150 is trained to predict consistent V_(base) to preserve theidentity shape over time, via a swapping loss function. Given tworandomly sampled frames, I^(i) and I^(j), the identity shapes V_(base)^(i) and V_(base) ^(j) are swapped and deformed using the originaloffsets ΔV^(i) and ΔV^(j) as:

$\begin{matrix}{{L_{s} = {{{niou}\left( {{\mathcal{R}\left( {{V_{base}^{j} + {\Delta V^{i}}},\ \theta^{i}} \right)},S^{i}} \right)} + {{niou}\left( {{\mathcal{R}\left( {{V_{base}^{i} + {\Delta V^{j}}},\ \theta^{j}} \right)},S^{j}} \right)}}},} & {{Eq}.\mspace{14mu}(3)}\end{matrix}$where θ^(i) and θ^(j) are the camera poses, ΔV^(i) and ΔV^(j) are themotion deformations, and S^(i) and S^(j) are the object silhouettes(masks) of frame i and j, respectively.

(·) denotes a general projection. In an embodiment,

(·) represents a differentiable renderer to render the 3D representationor 3D object to a 2D silhouette as

(V, θ). In an embodiment,

(·) represents a differentiable renderer to render a texture mesh to anRGB image as

(V, θ, I_(uv)) (note that mesh faces F are omitted for conciseness). Inan embodiment,

(·) represents a projection of a 3D point v to the image space as

(v, θ). The niou(·,·) denotes the negative intersection over union (IoU)objective. In an embodiment, the foreground masks used for thesilhouette are obtained by a segmentation model trained with the groundtruth foreground masks. The loss function L_(s) may be used to enforceconsistency between the identity shapes 142 and 146.

The neural network model 150 also predicts the texture images 154 and158 for the frames 132 and 136, respectively. Based on the observationthat object texture mapped to the UV space should be invariant to shapedeformation and stay constant over time, a texture invariance constraintmaybe used to encourage consistent texture reconstruction from allframes. However, naively aggregating the UV texture maps from all theframes may lead to a blurry video-level texture image. Instead, like theshape identity, texture consistency may be enforced between random pairsof frames, via a swap loss. Given two randomly sampled frames, I^(i) andI^(j), the texture images I_(uv) ^(i) and I_(uv) ^(j) are swapped andcombined with the original mesh reconstructions V^(i) and V^(j) as:

$\begin{matrix}{{L_{t} = {{d{{ist}\left( {{{\mathcal{R}\left( {V^{i},\theta^{i},I_{uv}^{j}} \right)} \odot S^{i}},{I^{i} \odot S^{i}}} \right)}} + {di{{st}\left( {{{\mathcal{R}\left( {V^{j},\theta^{j},I_{uv}^{i}} \right)} \odot S^{j}},{I^{j} \odot S^{j}}} \right)}}}},} & {{Eq}.\mspace{14mu}(4)}\end{matrix}$where dist(·,·) is a perceptual metric. The swapping technique enforcesconsistency of the texture images across the frames, thereby improvingthe accuracy of the predicted texture images 106, 3D shaperepresentations 108, and 3D objects 104 in FIG. 1A. For example, theloss function L_(t) may be used to enforce consistency between thetexture images 154 and 158. During inferencing, the neural network model150 may be fine-tuned on a particular video with the invarianceconstraints enforced by Equations (3) and (4). In an embodiment, theneural network model 150 is fine-tuned using self-supervision.Fine-tuning may improve the performance of the neural network model 150when domain differences in video quality, such as lighting conditions,etc. cause inconsistent 3D mesh reconstruction as each frame of a videois processed independently.

FIG. 1D illustrates a flowchart of a method 148 for applyingself-supervised adaptation to the system shown in FIG. 1A, in accordancewith an embodiment. The neural network model 150 is refined by takingadvantage of the redundancy in temporal sequences as a form ofself-supervision in order to improve the construction of dynamicnon-rigid objects. Although method 148 is described in the context of aneural network model, the method 148 may also be performed by a program,custom circuitry, or by a combination of custom circuitry and a program.For example, the method 148 may be executed by a GPU, CPU, or anyprocessor capable of implementing the neural network model. Furthermore,persons of ordinary skill in the art will understand that any systemthat performs method 148 is within the scope and spirit of embodimentsof the present invention.

At step 155, the neural network model 150 receives a sequence of framesfor a video including an object. At step 160, silhouettes and cameraposes are obtained. In an embodiment, the silhouettes are ground truthdata corresponding to the video. In an embodiment, the camera poses arepredicted by the neural network model 150. Steps 165 and 170 may beperformed in parallel or in sequence. At step 165, texture images arepredicted for each frame by the neural network model 150. At step 170,an identity shape and offsets are predicted for each frame by the neuralnetwork model 150.

At step 175, a loss function is computed based on texture invariance andshape identity invariance. In an embodiment, the loss function is aswapping loss function. In an embodiment, the loss function is acombination of Equations (3) and (4).

At step 180, if the error is reduced according to the loss function,then at step 190, refinement of the neural network model 150 iscompleted. Otherwise, at step 185, parameters of the neural networkmodel 150 are updated by back propagating the loss function before themethod 148 returns to step 155. In an embodiment, parameters of one ormore of the shape decoder 115, motion decoder 120, and texture decoder125 are updated.

In an embodiment, the texture image predicted for a first image istransferred to a second 3D shape representation predicted for a secondimage of the images in a video to produce a first 3D object. The first3D object is projected according to a first camera pose associated withthe first image to produce a first projected 3D object. A second textureimage predicted for the second image is transferred to the 3D shaperepresentation predicted for the first image to produce a second 3Dobject. The second 3D object is projected, according to a second camerapose associated with the second image, to produce a second projected 3Dobject and parameters of the neural network model 150 are updated toencourage consistency between the first projected 3D object and thesecond projected 3D object. In an embodiment, differences betweenoverlapped parts of the first and second image are reduced duringtraining.

In an embodiment, first non-rigid motion deformations predicted for thefirst image are applied to a first identity shape predicted for a secondimage of the images to produce a first 3D shape representation. Thefirst 3D shape representation is projected according to a first camerapose associated with the first image to produce a first projected 3Dobject. Second non-ridge motion deformations predicted for the secondimage are applied to a second identity shape predicted for the firstimage to produce a second 3D shape representation. The second 3D shaperepresentation is projected according to a second camera pose associatedwith the second image to produce a second projected 3D object andparameters of the neural network model 150 are updated to encourageconsistency between the first projected 3D object and the secondprojected 3D object.

FIG. 2A illustrates a block diagram of a training configuration for the3D object construction system 100 shown in FIG. 1A, in accordance withan embodiment. In addition to the blocks shown in FIG. 1A, the trainingframework includes a camera pose unit 225, a differentiable renderer222, and a loss function 215. In an embodiment, the camera pose unit 225is also included in the neural network model 150 and/or the 3D objectconstruction system 100. The camera pose unit 225 receives the featuresand predicts a camera pose (position and orientation), θ of the identityshape based on the features. The shape decoder 115, motion decoder 120,texture decoder 125, and camera pose unit 225 may be configured tojointly predict the identity shape, offsets, texture image, and camerapose.

The differentiable renderer 222 receives the 3D mesh and texture imageand renders the 3D mesh according to the predicted camera pose providedby the camera pose unit 225. In an embodiment, the texture image istransferred onto the 3D shape representation by the 3D mesh texture unit122 to construct the 3D object. The textured 3D object is then projectedby the differentiable renderer 222 according to the camera pose toproduce a rendered image.

Rendered images may be compared with the video frames and/or annotationsduring training. The loss function 215 may adjust parameters of theshape decoder 115, motion decoder 120, texture decoder 125, and/orcamera pose unit 225 to reduce differences between the rendered imagesand video frames. In some embodiments, the loss function 215 receivesground truth data, such as object silhouettes and/or keypointannotations, that are used to reduce differences between the renderedimages and video frames. In an embodiment, training is accomplished in aself-supervised manner using object silhouettes extracted fromcategory-specific images. The training configuration may be used totrain the 3D object construction system 100 using both individual inputimages and videos. In an embodiment, training may be performed using avariety of techniques, e.g., supervised, self-supervised, andsemi-supervised using individual images and/or videos. For example, the3D object construction system 100 may be trained to learn sets of shapebases using semi-supervised techniques with single-view input images,followed by fine-tuning via self-supervised techniques using anunlabeled video of a specific object.

Conventional techniques use annotated 2D object keypoints andcategory-level template shapes or silhouettes for training. However,scaling up learning with 2D annotations to hundreds of thousands ofimages is non-trivial and may also limit the generalization ability of atrained neural network model for new domains. For example, a 3Dconstruction neural network model that is conventionally trained onsingle-view images typically produces unstable and erratic predictionsfor video data. This is unsurprising, due to perturbations over time.Therefore, the temporal signal in videos should be used to provide anadvantage instead of a disadvantage.

A balance may be achieved between model generalization andspecialization. In an embodiment, an image-based neural network model istrained on a set of images, while at test time, the neural network modelwithin the 3D object construction system 100 is adapted or fine-tuned toan input video including a particular identity object. During test-timetraining no labels are provided for the video. Therefore,self-supervised objectives are introduced that can continuously improvethe neural network model. As previously described, the UV texture spaceprovides a parameterization that is invariant to object deformation.Object parts of an instance of an object should be constant when mappedfrom 2D via the predicted texture flow. Therefore, in addition toencouraging temporal consistency between the texture images and identityshapes predicted for different frames, temporal consistency may beencouraged between object parts in UV texture space. In particular,invariance loss may be used to train the camera pose unit 225 to predictthe camera position θ. Using the constraint of temporal consistency, therecovered identity shape and camera pose may be stabilized considerablyand adapted based on the video that is processed during fine-tuningand/or test time.

FIG. 2B illustrates a conceptual diagram of using temporal invariance toenforce parts correspondence, in accordance with an embodiment. Thevideo includes a bird object that the 3D object construction system 100may be trained to construct. Conventional techniques, such asunsupervised video correspondence (UVC) may be used to automaticallyapply (random) patterns to parts of the object in input video frames togenerate a propagated part map for each frame. The UVC model learns anaffinity matrix that captures pixel-level correspondences among videoframes. The UVC model can be used to propagate any annotation (e.g,segmentation labels, keypoints, part labels, etc.), from an annotatedkeyframe to the other unannotated frames. Part correspondence isgenerated within a video clip by “painting” a group of random regions onthe object on the first frame and propagating the part painting to therest of the video using the UVC model. Any specific part of the object,such as a wing may be painted as a single region or as multiple regions.In other words, the painting does not provide a semantic definition. Asshown in FIG. 2B, the painting appears as differently colored verticalstripes within a silhouette of the object visible in a propagated partmap 232.

In an embodiment, to obtain accurate part propagation of object parts bythe UVC model, two strategies may be employed. Firstly, parameters inthe 3D object construction system 100 may be fine-tuned on slidingwindows instead of all video frames. Each sliding window may includeN_(w)=50 consecutive frames and the sliding stride is set to N_(s)=10.The 3D object construction system 100 may be tuned for N_(t)=40iterations with frames in each sliding window. Secondly, instead of“painting” random parts onto the first frame and propagating the paintedparts to rest of the frames sequentially in a window, random parts maybe painted onto the middle frame (i.e. the N_(w)/2^(th) frame) in thewindow and the painted parts may be propagated backward to the firstframe as well as forward to the last frame in the window. The strategymay improve the propagation quality by decreasing the propagation rangeto half of the window size. Within each sliding window, consistency ofthe UV texture images, part UV maps, as well as identity shapes isencouraged for all of the frames.

A sequence of propagated part maps associated with video frames, such asthe propagated part map 232 associated with the frame 230 is processedby the 3D object construction system 100 in a test configuration. Theprocessing of the propagated part map 232 is described, however,additional propagated part maps shown in FIG. 2B or additional frames inthe sequence may be processed in a similar manner to produce additionalrendered images. In an embodiment, the part map is propagated across theobject in a number of the frames to produce propagated part maps. In anembodiment the number of frames is included in a sliding window.

The propagated part map for each frame is mapped to the UV texture spacewith the predicted texture flow 234 to produce part UV map 236. In anembodiment, the propagated part maps are mapped into the texture spaceaccording to corresponding texture flows predicted for the frames toproduce part maps in the texture space. In an embodiment, the part mapsare aggregated to produce a video level part UV map 235. By aggregatingthe part UV maps, i.e., averaging, noise is minimized in each individualpart UV map. The video-level part UV map 235 for the object depicted inthe video that will be constructed in 3D is shared by all frames in thevideo. Thus, for each frame, the video-level part UV map 235 is wrappedonto the predicted 3D shape representation 237. Aggregation of thepredicted parts in the UV texture space facilitates learning the camerapose by the 3D object construction system 100.

In an embodiment, 3D shape representations predicted for the frames arerendered according to the associated camera poses (not shown) and thevideo-level part map is transferred (e.g., wrapped) onto each one of the3D shape representations to produce rendered images. The wrapped 3Dshape representation 237 may be rendered by the differentiable renderer222 according to the predicted camera to produce rendered image 238.

Discrepancies between the parts rendered back to the 2D space and thepart propagations, for each frame, are penalized. In an embodiment, theloss function 215 may be configured to update parameters of the neuralnetwork model 150 based on the discrepancies. In an embodiment, theparameters are updated to encourage consistency between the renderedimages and the propagated part maps.

As the propagated part maps are usually temporally smooth andcontinuous, the loss implicitly regularizes the 3D object constructionsystem 100 to predict coherent camera pose and object shape over time.In an embodiment, instead of minimizing the discrepancy between therendered part map (e.g., rendered image, such as the rendered image 238)and the propagated part map of a frame, it may be more robust topenalize the geometric distance between the projections of verticesassigned to each part with 2D points sampled from the correspondingpart. A Chamfer loss may be computed as:

$\begin{matrix}{{L_{c} = {\sum_{j = 1}^{N_{f}}{\sum_{i = 1}^{N_{p}}{\frac{1}{V_{i}^{j}}\mspace{14mu}{Chamfer}\mspace{14mu}\left( {{\mathcal{R}\left( {V_{i}^{j},\theta^{j}} \right)},Y_{i}^{j}} \right)}}}},} & {{Eq}.\mspace{14mu}(5)}\end{matrix}$where N_(f) is the number of frames in the video, N_(p)=6 is the numberof parts, and V_(i) ^(j) are vertices assigned to part i. The Chamferdistance is used to compute the loss because the vertex projections

(V_(i) ^(j),θ^(j)) are not strictly one-to-one corresponded to thesampled 2D points Y_(i) ^(j).

The input samples of the propagated part maps are compared with theprojected samples of the images rendered based on the predictedvideo-level part UV map, 3D representation, and camera pose to calculatethe Chamfer loss. The Chamfer loss reduces errors from the camera poseestimation. Alternatively, more samples (e.g., pixels) within thecolored parts of the propagated part map 232 may be used as the groundtruth and the predicted 3D shape representation 237 may be wrapped withthe video-level part UV map 235 and rendered according to the camerapose to produce a rendered image 238 for comparison with the groundtruth colored samples.

In an embodiment, the colored parts are used to supervise the estimatedcamera pose without rendering images. The input video frames may besampled within each part of the part propagations and then compared withthe predicted samples on the 3D representations (e.g., meshes) projectedaccording to the predicted camera pose (no rendering).

Another technique for training uses propagation of the ground truthannotations, such as keypoints, from the input images through thepredicted texture images to the rendered images. A loss may be computedbased on the ground truth annotations and the rendered annotations. Theground truth annotations enable correspondences to be established acrossdifferent instances in a set of shape bases. For example, a beak orwingtip is labeled as a keypoint in different images of birds anddifferent birds with similar shapes are clustered together to form a setof shape bases.

FIG. 2C illustrates a conceptual diagram of training using annotationre-projection, in accordance with an embodiment. Although the techniqueis described for annotations that are keypoints, those skilled in theart will recognize that other types of annotations may be used with thetechnique. When the 3D object construction system 100 is trained usingweak supervision, 2D keypoints may be provided as ground truthannotations that semantically associate different instances of theobject. For example, an annotated frame 240 includes multiple keypoints,such as a tail keypoint 248 at the tip of the bird's tail.

When the 2D keypoints are projected onto the 3D representation (e.g.,mesh surface), the same semantic keypoint for different object instancesshould be matched to the same face on the mesh surface. Conventionally,to model the mapping between the 3D mesh surface and the 2D keypoints,an affinity matrix is learned that describes the probability of each 2Dkeypoint mapping to each vertex on the 3D representation. Theprobability map is a heatmap, such as heatmap 242.

The affinity matrix is shared among all instances and is independent ofindividual shape variations. The conventional approach is sub-optimalbecause: (i) Mesh vertices are a subset of discrete points on acontinuous mesh surface and so their weighted combination defined by theaffinity matrix may not lie on the mesh surface, leading to inaccuratemappings of 2D keypoints. (ii) The mapping from the image space to themesh surface is described by the affinity matrix. In contrast, becausethe mapping of image space the mesh surface is already modeled by thetexture flow, it is potentially redundant to independently learn boththe affinity matrix and the texture flow.

Therefore, the texture flow is re-utilized to map 2D keypoints from eachimage to the mesh surface. For example, the 2D tail keypoint 248 ismapped to a 3D tail keypoint 246 on the 3D representation. First, each2D keypoint in an annotated frame is mapped to the UV texture space thatis independent of shape deformation. For example, the keypoints in theannotated frame 240 may be mapped to the UV texture space according tothe texture flow, I_(flow) that is predicted for the annotated frame 240to generate an annotation map in the texture space, keypoint UV map 244.Ideally, each semantic keypoint from different instances of the objectshould map to the same point in the UV space. In practice, this may nothold due to inaccurate texture flow prediction. To accurately map eachkeypoint to the UV space, a canonical keypoint UV map 245 may becomputed by: (i) mapping the keypoint heat map for each instance to theUV space via the predicted texture flow, and (ii) aggregating thekeypoint UV maps across all instances to eliminate outliers caused byincorrect texture flow prediction. The keypoint UV maps are aggregated(e.g., averaged) to produce the canonical keypoint UV map 245.

The canonical keypoint UV map 245 is then transferred to 3D shaperepresentations predicted for the frames to produce annotated 3D shaperepresentations. The annotated 3D shape representations may then beprojected, according to associated camera poses, to produce projectedannotations for the frames. In an embodiment, the projection comprisesrendering. In an embodiment, the keypoint re-projection is done by (i)warping the canonical keypoint UV map to each individual predicted meshsurface to produce a 3D keypoint; (ii) projecting the 3D keypoints KL)back to the 2D space via the predicted camera pose to producere-projected 2D keypoints; (iii) comparing the re-projected 2D keypointsagainst the ground truth keypoints in 2D, K_(2D) ^(i). For example, thecanonical keypoint UV map 245 is mapped to the 3D representation toproduce 3D keypoint 246, including the tail keypoint 250. The 3Dkeypoints 246 are re-projected according to the predicted camera poseand compared with the ground truth keypoints in the annotated frame 240.In an embodiment, parameters of the neural network model 150 are updatedto reduce differences and encourage consistency between the projectedkeypoints and the ground truth keypoints.

Given the 3D correspondence (denoted as K_(3D) ^(i) of each 2D semantickeypoint K_(2D) ^(i), a keypoint re-projection loss enforces theprojection of the former to be consistent with the latter by:

$\begin{matrix}{{L_{kp} = {\frac{1}{N_{k}}{\sum_{i = 1}^{N_{k}}{{{\mathcal{R}\left( {K_{3D}^{i},\theta} \right)} - K_{2D}^{i}}}}}},} & {{Eq}.\mspace{14mu}(6)}\end{matrix}$where N_(k) is the number of keypoints. Evaluation of the keypointre-projection loss function implicitly reveals the correctness of boththe predicted shape and camera pose for the mesh reconstructionalgorithm, especially for objects that do not have 3D ground truthannotations. The re-projection of annotations enables weakly supervisedtraining of the 3D object construction system 100.

One bottleneck of conventional image-based 3D mesh reconstructionmethods is that the predicted shapes are assumed to be symmetric. Thisassumption does not hold for most non-rigid animals, e.g., birds tiltingtheir heads, or walking horses, etc. Therefore, the assumption ofsymmetry may be ignored and the reconstructed mesh representations maybe allowed to fit more complex, non-rigid poses via anas-rigid-as-possible (ARAP) constraint. The ARAP constraint is anadditional loss objective that may be used for self-supervised trainingof the 3D object construction system 100. The identity shape is smoothby construction. However, application of the offsets predicted by themotion decoder 120 may produce discontinuities in the 3D meshrepresentation. The ARAP constraint is used to ensure that edge lengthsof the 3D mesh are maintained even when the 3D mesh is rotated. ARAP isa self-supervised regularization that maintains rigidity and can be usedon individual input images and video sequences.

Without any pose-related regularization, the predicted motiondeformation ΔV often leads to erroneous random deformations and spikeson the surface of the 3D mesh, which do not faithfully describe themotion of a non-rigid object. The ARAP constraint encourages rigidity oflocal transformations and the preservation of the local mesh structure.The ARAP constraint is formulated as an objective that ensures that thepredicted shape V is a locally rigid transformation from the predictedbase shape V_(base) by:

$\begin{matrix}{{{L_{arap}\left( {V_{base},V} \right)} = {\sum_{i = 1}^{V}{\sum_{j \in \mathcal{N}_{(i)}}{w_{ij}{{\left( {V^{i} - V^{j}} \right) - {R_{i}\left( {V_{base}^{i} - V_{base}^{j}} \right)}}}}}}},} & {{Eq}.\mspace{14mu}(7)}\end{matrix}$where

represents the neighboring vertices of a vertex i, w_(ij) and R_(i), arethe cotangent weight and the best approximating rotation matrix,respectively. As another constraint that does not require any labels,ARAP may be enforced during test-time training with video inputs, toimprove shape prediction.

In an embodiment, parameters of the neural network model 150 are updatedbased on the ARAP loss function L_(arap) to reduce discontinuities inthe predicted 3D shape representations. Non-rigid motion deformations ofthe 3D shape representations are predicted for the frames and applied toidentity shapes predicted for the frames to produce the 3D shaperepresentations of the object. The ARAP loss function may be evaluatedbased on rotated differences between the identity shapes and differencesbetween the 3D shape representations.

In an embodiment, two image-based 3D object construction methods areused for training the neural network model 150 (i) a weakly supervisedmethod (i.e., with object silhouettes and 2D annotations provided), and(ii) a self-supervised method where only object silhouettes areavailable. The image-based trained neural network model 150 is thenadapted to videos. For example, in an embodiment, the trained neuralnetwork model 150 is adapted to videos that are in-the-wild bird andzebra videos.

FIG. 2D illustrates a flowchart of a method 255 for training the 3Dobject construction system 100 shown in FIG. 1A, in accordance with anembodiment. At step 260, the 3D object construction system 100 istrained to learn a set of shape bases from single-view images. In anembodiment, objectives used individually or in combination for thesingle-view construction include (i) foreground mask loss: a negativeintersection over union objective between rendered and ground truthsilhouettes; (ii) foreground RGB texture loss: a perceptual metricbetween rendered and input RGB images; (iii) mesh smoothness: aLaplacian constraint to encourage smooth mesh reconstruction; (iv)keypoint re-projection loss; and (v) the ARAP constraint.

At step 270, the 3D object construction system 100 is trained usingself-supervision for videos. Since it is feasible to predict asegmentation mask via a pretrained segmentation model, the predictedforeground mask may be used to compute the foreground mask loss. In anembodiment, objectives used individually or in combination for the forvideos may include the foreground RGB texture loss, and the meshsmoothness objective. In an embodiment, the ARAP constraint may also beused.

At step 275, the 3D object construction system 100 is fine-tuned forconstructing a particular 3D object using the one or more of theinvariance constraints for texture, identity shape, and partcorrespondence.

FIG. 3 illustrates images and reconstructed objects, in accordance withan embodiment. The 2D bird object shown in images 300, 310, and 320 isconstructed in 3D using the 3D object construction system 100 includingthe camera pose unit 225 trained on single-view images to produce the 3Drepresentations and objects 305, trained on single view images andvideos, but without invariance constraints, to produce the 3Drepresentations and objects 315, and trained on single view images andvideos with invariance constraints to produce the 3D representations andobjects 325. Note that the object shape, camera pose, and texture isless reliably predicted for the 3D representations and objects 315compared with 325. Similarly, the object shape, camera pose, and textureare less reliably predicted for the 3D representations and objects 305compared with 315. In sum, the 3D object construction algorithm recoverstemporally consistent and reliable 3D structures from videos ofnon-rigid objects including those of animals captured in the wild.

The 3D object construction technique does not require pre-definedtemplate object meshes, annotations of a 3D object, 2D annotations, orcamera pose for the video frames. The video-based 3D object constructionsystem 100 may be refined via self-supervised online adaptation for anyincoming test video. First, a category-specific 3D construction neuralnetwork model 150 is learned from a collection of single-view images ofthe same category. The 3D object construction system 100 jointlypredicts the shape, texture, and camera pose of an object in an image.Then, at inference time, the neural network model is fine-tuned overtime by an object-specific test video using self-supervisedregularization terms that exploit temporal consistency of an objectinstance to enforce that all reconstructed meshes share a common texturemap, a base shape, as well as parts.

The 3D object construction technique may be used for content creation,such as generation of 3D characters for games, movies, and 3D printing.Because the 3D characters are generated from video, the content may alsoinclude motion of the character, as predicted based on the video.Compared with conventional solutions, the 3D object constructiontechnique does not rely on a pre-defined parametric mesh (e.g., humanobject defined by a fixed number of vertices). The 3D objectconstruction system also generalizes well, particularly for non-rigidobjects.

Parallel Processing Architecture

FIG. 4 illustrates a parallel processing unit (PPU) 400, in accordancewith an embodiment. The PPU 400 may be used to implement the 3D objectconstruction system 100. The PPU 400 may be used to implement one ormore of the encoder 105, shape decoder 115, motion decoder 120, texturedecoder 125, 3D mesh construction unit 130, and 3D mesh texture unit 122within the server/client system 100.

In an embodiment, the PPU 400 is a multi-threaded processor that isimplemented on one or more integrated circuit devices. The PPU 400 is alatency hiding architecture designed to process many threads inparallel. A thread (e.g., a thread of execution) is an instantiation ofa set of instructions configured to be executed by the PPU 400. In anembodiment, the PPU 400 is a graphics processing unit (GPU) configuredto implement a graphics rendering pipeline for processingthree-dimensional (3D) graphics data in order to generatetwo-dimensional (2D) image data for display on a display device. Inother embodiments, the PPU 400 may be utilized for performinggeneral-purpose computations. While one exemplary parallel processor isprovided herein for illustrative purposes, it should be strongly notedthat such processor is set forth for illustrative purposes only, andthat any processor may be employed to supplement and/or substitute forthe same.

One or more PPUs 400 may be configured to accelerate thousands of HighPerformance Computing (HPC), data center, cloud computing, and machinelearning applications. The PPU 400 may be configured to acceleratenumerous deep learning systems and applications including autonomousvehicle platforms, deep learning, high-accuracy speech, image, and textrecognition systems, intelligent video analytics, molecular simulations,drug discovery, disease diagnosis, weather forecasting, big dataanalytics, astronomy, molecular dynamics simulation, financial modeling,robotics, factory automation, real-time language translation, onlinesearch optimizations, and personalized user recommendations, and thelike.

As shown in FIG. 4 , the PPU 400 includes an Input/Output (I/O) unit405, a front end unit 415, a scheduler unit 420, a work distributionunit 425, a hub 430, a crossbar (Xbar) 470, one or more generalprocessing clusters (GPCs) 450, and one or more memory partition units480. The PPU 400 may be connected to a host processor or other PPUs 400via one or more high-speed NVLink 410 interconnect. The PPU 400 may beconnected to a host processor or other peripheral devices via aninterconnect 402. The PPU 400 may also be connected to a local memory404 comprising a number of memory devices. In an embodiment, the localmemory may comprise a number of dynamic random access memory (DRAM)devices. The DRAM devices may be configured as a high-bandwidth memory(HBM) subsystem, with multiple DRAM dies stacked within each device.

The NVLink 410 interconnect enables systems to scale and include one ormore PPUs 400 combined with one or more CPUs, supports cache coherencebetween the PPUs 400 and CPUs, and CPU mastering. Data and/or commandsmay be transmitted by the NVLink 410 through the hub 430 to/from otherunits of the PPU 400 such as one or more copy engines, a video encoder,a video decoder, a power management unit, etc. (not explicitly shown).The NVLink 410 is described in more detail in conjunction with FIG. 5B.

The I/O unit 405 is configured to transmit and receive communications(e.g., commands, data, etc.) from a host processor (not shown) over theinterconnect 402. The I/O unit 405 may communicate with the hostprocessor directly via the interconnect 402 or through one or moreintermediate devices such as a memory bridge. In an embodiment, the I/Ounit 405 may communicate with one or more other processors, such as oneor more the PPUs 400 via the interconnect 402. In an embodiment, the I/Ounit 405 implements a Peripheral Component Interconnect Express (PCIe)interface for communications over a PCIe bus and the interconnect 402 isa PCIe bus. In alternative embodiments, the I/O unit 405 may implementother types of well-known interfaces for communicating with externaldevices.

The I/O unit 405 decodes packets received via the interconnect 402. Inan embodiment, the packets represent commands configured to cause thePPU 400 to perform various operations. The I/O unit 405 transmits thedecoded commands to various other units of the PPU 400 as the commandsmay specify. For example, some commands may be transmitted to the frontend unit 415. Other commands may be transmitted to the hub 430 or otherunits of the PPU 400 such as one or more copy engines, a video encoder,a video decoder, a power management unit, etc. (not explicitly shown).In other words, the I/O unit 405 is configured to route communicationsbetween and among the various logical units of the PPU 400.

In an embodiment, a program executed by the host processor encodes acommand stream in a buffer that provides workloads to the PPU 400 forprocessing. A workload may comprise several instructions and data to beprocessed by those instructions. The buffer is a region in a memory thatis accessible (e.g., read/write) by both the host processor and the PPU400. For example, the I/O unit 405 may be configured to access thebuffer in a system memory connected to the interconnect 402 via memoryrequests transmitted over the interconnect 402. In an embodiment, thehost processor writes the command stream to the buffer and thentransmits a pointer to the start of the command stream to the PPU 400.The front end unit 415 receives pointers to one or more command streams.The front end unit 415 manages the one or more streams, reading commandsfrom the streams and forwarding commands to the various units of the PPU400.

The front end unit 415 is coupled to a scheduler unit 420 thatconfigures the various GPCs 450 to process tasks defined by the one ormore streams. The scheduler unit 420 is configured to track stateinformation related to the various tasks managed by the scheduler unit420. The state may indicate which GPC 450 a task is assigned to, whetherthe task is active or inactive, a priority level associated with thetask, and so forth. The scheduler unit 420 manages the execution of aplurality of tasks on the one or more GPCs 450.

The scheduler unit 420 is coupled to a work distribution unit 425 thatis configured to dispatch tasks for execution on the GPCs 450. The workdistribution unit 425 may track a number of scheduled tasks receivedfrom the scheduler unit 420. In an embodiment, the work distributionunit 425 manages a pending task pool and an active task pool for each ofthe GPCs 450. As a GPC 450 finishes the execution of a task, that taskis evicted from the active task pool for the GPC 450 and one of theother tasks from the pending task pool is selected and scheduled forexecution on the GPC 450. If an active task has been idle on the GPC450, such as while waiting for a data dependency to be resolved, thenthe active task may be evicted from the GPC 450 and returned to thepending task pool while another task in the pending task pool isselected and scheduled for execution on the GPC 450.

In an embodiment, a host processor executes a driver kernel thatimplements an application programming interface (API) that enables oneor more applications executing on the host processor to scheduleoperations for execution on the PPU 400. In an embodiment, multiplecompute applications are simultaneously executed by the PPU 400 and thePPU 400 provides isolation, quality of service (QoS), and independentaddress spaces for the multiple compute applications. An application maygenerate instructions (e.g., API calls) that cause the driver kernel togenerate one or more tasks for execution by the PPU 400. The driverkernel outputs tasks to one or more streams being processed by the PPU400. Each task may comprise one or more groups of related threads,referred to herein as a warp. In an embodiment, a warp comprises 32related threads that may be executed in parallel. Cooperating threadsmay refer to a plurality of threads including instructions to performthe task and that may exchange data through shared memory. The tasks maybe allocated to one or more processing units within a GPC 450 andinstructions are scheduled for execution by at least one warp.

The work distribution unit 425 communicates with the one or more GPCs450 via XBar 470. The XBar 470 is an interconnect network that couplesmany of the units of the PPU 400 to other units of the PPU 400. Forexample, the XBar 470 may be configured to couple the work distributionunit 425 to a particular GPC 450. Although not shown explicitly, one ormore other units of the PPU 400 may also be connected to the XBar 470via the hub 430.

The tasks are managed by the scheduler unit 420 and dispatched to a GPC450 by the work distribution unit 425. The GPC 450 is configured toprocess the task and generate results. The results may be consumed byother tasks within the GPC 450, routed to a different GPC 450 via theXBar 470, or stored in the memory 404. The results can be written to thememory 404 via the memory partition units 480, which implement a memoryinterface for reading and writing data to/from the memory 404. Theresults can be transmitted to another PPU 400 or CPU via the NVLink 410.In an embodiment, the PPU 400 includes a number U of memory partitionunits 480 that is equal to the number of separate and distinct memorydevices of the memory 404 coupled to the PPU 400. Each GPC 450 mayinclude a memory management unit to provide translation of virtualaddresses into physical addresses, memory protection, and arbitration ofmemory requests. In an embodiment, the memory management unit providesone or more translation lookaside buffers (TLBs) for performingtranslation of virtual addresses into physical addresses in the memory404.

In an embodiment, the memory partition unit 480 includes a RasterOperations (ROP) unit, a level two (L2) cache, and a memory interfacethat is coupled to the memory 404. The memory interface may implement32, 64, 128, 1024-bit data buses, or the like, for high-speed datatransfer. The PPU 400 may be connected to up to Y memory devices, suchas high bandwidth memory stacks or graphics double-data-rate, version 5,synchronous dynamic random access memory, or other types of persistentstorage. In an embodiment, the memory interface implements an HBM2memory interface and Y equals half U. In an embodiment, the HBM2 memorystacks are located on the same physical package as the PPU 400,providing substantial power and area savings compared with conventionalGDDR5 SDRAM systems. In an embodiment, each HBM2 stack includes fourmemory dies and Y equals 4, with each HBM2 stack including two 128-bitchannels per die for a total of 8 channels and a data bus width of 1024bits.

In an embodiment, the memory 404 supports Single-Error CorrectingDouble-Error Detecting (SECDED) Error Correction Code (ECC) to protectdata. ECC provides higher reliability for compute applications that aresensitive to data corruption. Reliability is especially important inlarge-scale cluster computing environments where PPUs 400 process verylarge datasets and/or run applications for extended periods.

In an embodiment, the PPU 400 implements a multi-level memory hierarchy.In an embodiment, the memory partition unit 480 supports a unifiedmemory to provide a single unified virtual address space for CPU and PPU400 memory, enabling data sharing between virtual memory systems. In anembodiment the frequency of accesses by a PPU 400 to memory located onother processors is traced to ensure that memory pages are moved to thephysical memory of the PPU 400 that is accessing the pages morefrequently. In an embodiment, the NVLink 410 supports addresstranslation services allowing the PPU 400 to directly access a CPU'spage tables and providing full access to CPU memory by the PPU 400.

In an embodiment, copy engines transfer data between multiple PPUs 400or between PPUs 400 and CPUs. The copy engines can generate page faultsfor addresses that are not mapped into the page tables. The memorypartition unit 480 can then service the page faults, mapping theaddresses into the page table, after which the copy engine can performthe transfer. In a conventional system, memory is pinned (e.g.,non-pageable) for multiple copy engine operations between multipleprocessors, substantially reducing the available memory. With hardwarepage faulting, addresses can be passed to the copy engines withoutworrying if the memory pages are resident, and the copy process istransparent.

Data from the memory 404 or other system memory may be fetched by thememory partition unit 480 and stored in the L2 cache 460, which islocated on-chip and is shared between the various GPCs 450. As shown,each memory partition unit 480 includes a portion of the L2 cacheassociated with a corresponding memory 404. Lower level caches may thenbe implemented in various units within the GPCs 450. For example, eachof the processing units within a GPC 450 may implement a level one (L1)cache. The L1 cache is private memory that is dedicated to a particularprocessing unit. The L2 cache 460 is coupled to the memory interface 470and the XBar 470 and data from the L2 cache may be fetched and stored ineach of the L1 caches for processing.

In an embodiment, the processing units within each GPC 450 implement aSIMD (Single-Instruction, Multiple-Data) architecture where each threadin a group of threads (e.g., a warp) is configured to process adifferent set of data based on the same set of instructions. All threadsin the group of threads execute the same instructions. In anotherembodiment, the processing unit implements a SIMT (Single-Instruction,Multiple Thread) architecture where each thread in a group of threads isconfigured to process a different set of data based on the same set ofinstructions, but where individual threads in the group of threads areallowed to diverge during execution. In an embodiment, a programcounter, call stack, and execution state is maintained for each warp,enabling concurrency between warps and serial execution within warpswhen threads within the warp diverge. In another embodiment, a programcounter, call stack, and execution state is maintained for eachindividual thread, enabling equal concurrency between all threads,within and between warps. When execution state is maintained for eachindividual thread, threads executing the same instructions may beconverged and executed in parallel for maximum efficiency.

Cooperative Groups is a programming model for organizing groups ofcommunicating threads that allows developers to express the granularityat which threads are communicating, enabling the expression of richer,more efficient parallel decompositions. Cooperative launch APIs supportsynchronization amongst thread blocks for the execution of parallelalgorithms. Conventional programming models provide a single, simpleconstruct for synchronizing cooperating threads: a barrier across allthreads of a thread block (e.g., the syncthreads( ) function). However,programmers would often like to define groups of threads at smaller thanthread block granularities and synchronize within the defined groups toenable greater performance, design flexibility, and software reuse inthe form of collective group-wide function interfaces.

Cooperative Groups enables programmers to define groups of threadsexplicitly at sub-block (e.g., as small as a single thread) andmulti-block granularities, and to perform collective operations such assynchronization on the threads in a cooperative group. The programmingmodel supports clean composition across software boundaries, so thatlibraries and utility functions can synchronize safely within theirlocal context without having to make assumptions about convergence.Cooperative Groups primitives enable new patterns of cooperativeparallelism, including producer-consumer parallelism, opportunisticparallelism, and global synchronization across an entire grid of threadblocks.

Each processing unit includes a large number (e.g., 128, etc.) ofdistinct processing cores (e.g., functional units) that may befully-pipelined, single-precision, double-precision, and/or mixedprecision and include a floating point arithmetic logic unit and aninteger arithmetic logic unit. In an embodiment, the floating pointarithmetic logic units implement the IEEE 754-2008 standard for floatingpoint arithmetic. In an embodiment, the cores include 64single-precision (32-bit) floating point cores, 64 integer cores, 32double-precision (64-bit) floating point cores, and 8 tensor cores.

Tensor cores configured to perform matrix operations. In particular, thetensor cores are configured to perform deep learning matrix arithmetic,such as convolution operations for neural network training andinferencing. In an embodiment, each tensor core operates on a 4×4 matrixand performs a matrix multiply and accumulate operation D=A×B+C, whereA, B, C, and D are 4×4 matrices.

In an embodiment, the matrix multiply inputs A and B are 16-bit floatingpoint matrices, while the accumulation matrices C and D may be 16-bitfloating point or 32-bit floating point matrices. Tensor Cores operateon 16-bit floating point input data with 32-bit floating pointaccumulation. The 16-bit floating point multiply requires 64 operationsand results in a full precision product that is then accumulated using32-bit floating point addition with the other intermediate products fora 4×4×4 matrix multiply. In practice, Tensor Cores are used to performmuch larger two-dimensional or higher dimensional matrix operations,built up from these smaller elements. An API, such as CUDA 9 C++ API,exposes specialized matrix load, matrix multiply and accumulate, andmatrix store operations to efficiently use Tensor Cores from a CUDA-C++program. At the CUDA level, the warp-level interface assumes 16×16 sizematrices spanning all 32 threads of the warp.

Each processing unit may also comprise M special function units (SFUs)that perform special functions (e.g., attribute evaluation, reciprocalsquare root, and the like). In an embodiment, the SFUs may include atree traversal unit configured to traverse a hierarchical tree datastructure. In an embodiment, the SFUs may include texture unitconfigured to perform texture map filtering operations. In anembodiment, the texture units are configured to load texture maps (e.g.,a 2D array of texels) from the memory 404 and sample the texture maps toproduce sampled texture values for use in shader programs executed bythe processing unit. In an embodiment, the texture maps are stored inshared memory that may comprise or include an L1 cache. The textureunits implement texture operations such as filtering operations usingmip-maps (e.g., texture maps of varying levels of detail). In anembodiment, each processing unit includes two texture units.

Each processing unit also comprises N load store units (LSUs) thatimplement load and store operations between the shared memory and theregister file. Each processing unit includes an interconnect networkthat connects each of the cores to the register file and the LSU to theregister file, shared memory. In an embodiment, the interconnect networkis a crossbar that can be configured to connect any of the cores to anyof the registers in the register file and connect the LSUs to theregister file and memory locations in shared memory.

The shared memory is an array of on-chip memory that allows for datastorage and communication between the processing units and betweenthreads within a processing unit. In an embodiment, the shared memorycomprises 128 KB of storage capacity and is in the path from each of theprocessing units to the memory partition unit 480. The shared memory canbe used to cache reads and writes. One or more of the shared memory, L1cache, L2 cache, and memory 404 are backing stores.

Combining data cache and shared memory functionality into a singlememory block provides the best overall performance for both types ofmemory accesses. The capacity is usable as a cache by programs that donot use shared memory. For example, if shared memory is configured touse half of the capacity, texture and load/store operations can use theremaining capacity. Integration within the shared memory enables theshared memory to function as a high-throughput conduit for streamingdata while simultaneously providing high-bandwidth and low-latencyaccess to frequently reused data.

When configured for general purpose parallel computation, a simplerconfiguration can be used compared with graphics processing.Specifically, fixed function graphics processing units, are bypassed,creating a much simpler programming model. In the general purposeparallel computation configuration, the work distribution unit 425assigns and distributes blocks of threads directly to the processingunits within the GPCs 450. Threads execute the same program, using aunique thread ID in the calculation to ensure each thread generatesunique results, using the processing unit(s) to execute the program andperform calculations, shared memory to communicate between threads, andthe LSU to read and write global memory through the shared memory andthe memory partition unit 480. When configured for general purposeparallel computation, the processing units can also write commands thatthe scheduler unit 420 can use to launch new work on the processingunits.

The PPUs 430 may each include, and/or be configured to perform functionsof, one or more processing cores and/or components thereof, such asTensor Cores (TCs), Tensor Processing Units(TPUs), Pixel Visual Cores(PVCs), Vision Processing Units (VPUs), Graphics Processing Clusters(GPCs), Texture Processing Clusters (TPCs), Streaming Multiprocessors(SMs), Tree Traversal Units (TTUs), Artificial Intelligence Accelerators(AIAs), Deep Learning Accelerators (DLAs), Arithmetic-Logic Units(ALUs), Application-Specific Integrated Circuits (ASICs), Floating PointUnits (FPUs), input/output (I/O) elements, peripheral componentinterconnect (PCI) or peripheral component interconnect express (PCIe)elements, and/or the like.

The PPU 400 may be included in a desktop computer, a laptop computer, atablet computer, servers, supercomputers, a smart-phone (e.g., awireless, hand-held device), personal digital assistant (PDA), a digitalcamera, a vehicle, a head mounted display, a hand-held electronicdevice, and the like. In an embodiment, the PPU 400 is embodied on asingle semiconductor substrate. In another embodiment, the PPU 400 isincluded in a system-on-a-chip (SoC) along with one or more otherdevices such as additional PPUs 400, the memory 404, a reducedinstruction set computer (RISC) CPU, a memory management unit (MMU), adigital-to-analog converter (DAC), and the like.

In an embodiment, the PPU 400 may be included on a graphics card thatincludes one or more memory devices. The graphics card may be configuredto interface with a PCIe slot on a motherboard of a desktop computer. Inyet another embodiment, the PPU 400 may be an integrated graphicsprocessing unit (iGPU) or parallel processor included in the chipset ofthe motherboard.

Exemplary Computing System

Systems with multiple GPUs and CPUs are used in a variety of industriesas developers expose and leverage more parallelism in applications suchas artificial intelligence computing. High-performance GPU-acceleratedsystems with tens to many thousands of compute nodes are deployed indata centers, research facilities, and supercomputers to solve everlarger problems. As the number of processing devices within thehigh-performance systems increases, the communication and data transfermechanisms need to scale to support the increased bandwidth.

FIG. 5A is a conceptual diagram of a processing system 500 implementedusing the PPU 400 of FIG. 3 , in accordance with an embodiment. Theexemplary system 565 may be configured to implement the method 130 shownin FIG. 1B, the method 148 shown in FIG. 1D, and/or the method 255 shownin FIG. 2D. The processing system 500 includes a CPU 530, switch 510,and multiple PPUs 400, and respective memories 404. The NVLink 410provides high-speed communication links between each of the PPUs 400.Although a particular number of NVLink 410 and interconnect 402connections are illustrated in FIG. 5A, the number of connections toeach PPU 400 and the CPU 530 may vary. The switch 510 interfaces betweenthe interconnect 402 and the CPU 530. The PPUs 400, memories 404, andNVLinks 410 may be situated on a single semiconductor platform to form aparallel processing module 525. In an embodiment, the switch 510supports two or more protocols to interface between various differentconnections and/or links.

In another embodiment (not shown), the NVLink 410 provides one or morehigh-speed communication links between each of the PPUs 400 and the CPU530 and the switch 510 interfaces between the interconnect 402 and eachof the PPUs 400. The PPUs 400, memories 404, and interconnect 402 may besituated on a single semiconductor platform to form a parallelprocessing module 525. In yet another embodiment (not shown), theinterconnect 402 provides one or more communication links between eachof the PPUs 400 and the CPU 530 and the switch 510 interfaces betweeneach of the PPUs 400 using the NVLink 410 to provide one or morehigh-speed communication links between the PPUs 400. In anotherembodiment (not shown), the NVLink 410 provides one or more high-speedcommunication links between the PPUs 400 and the CPU 530 through theswitch 510. In yet another embodiment (not shown), the interconnect 302provides one or more communication links between each of the PPUs 400directly. One or more of the NVLink 410 high-speed communication linksmay be implemented as a physical NVLink interconnect or either anon-chip or on-die interconnect using the same protocol as the NVLink410.

In the context of the present description, a single semiconductorplatform may refer to a sole unitary semiconductor-based integratedcircuit fabricated on a die or chip. It should be noted that the termsingle semiconductor platform may also refer to multi-chip modules withincreased connectivity which simulate on-chip operation and makesubstantial improvements over utilizing a conventional busimplementation. Of course, the various circuits or devices may also besituated separately or in various combinations of semiconductorplatforms per the desires of the user. Alternately, the parallelprocessing module 525 may be implemented as a circuit board substrateand each of the PPUs 400 and/or memories 404 may be packaged devices. Inan embodiment, the CPU 530, switch 510, and the parallel processingmodule 525 are situated on a single semiconductor platform.

In an embodiment, the signaling rate of each NVLink 410 is 20 to 25Gigabits/second and each PPU 400 includes six NVLink 410 interfaces (asshown in FIG. 5A, five NVLink 410 interfaces are included for each PPU400). Each NVLink 410 provides a data transfer rate of 25Gigabytes/second in each direction, with six links providing 300Gigabytes/second. The NVLinks 410 can be used exclusively for PPU-to-PPUcommunication as shown in FIG. 5A, or some combination of PPU-to-PPU andPPU-to-CPU, when the CPU 530 also includes one or more NVLink 410interfaces.

In an embodiment, the NVLink 410 allows direct load/store/atomic accessfrom the CPU 530 to each PPU's 400 memory 404. In an embodiment, theNVLink 410 supports coherency operations, allowing data read from thememories 404 to be stored in the cache hierarchy of the CPU 530,reducing cache access latency for the CPU 530. In an embodiment, theNVLink 410 includes support for Address Translation Services (ATS),allowing the PPU 400 to directly access page tables within the CPU 530.One or more of the NVLinks 410 may also be configured to operate in alow-power mode.

FIG. 5B illustrates an exemplary system 565 in which the variousarchitecture and/or functionality of the various previous embodimentsmay be implemented. The exemplary system 565 may be configured toimplement the method 130 shown in FIG. 1B, the method 148 shown in FIG.1D, and/or the method 255 shown in FIG. 2D.

As shown, a system 565 is provided including at least one centralprocessing unit 530 that is connected to a communication bus 575. Thecommunication bus 575 may directly or indirectly couple one or more ofthe following devices: main memory 540, network interface 535, CPU(s)530, display device(s) 545, input device(s) 560, switch 510, andparallel processing system 525. The communication bus 575 may beimplemented using any suitable protocol and may represent one or morelinks or busses, such as an address bus, a data bus, a control bus, or acombination thereof. The communication bus 575 may include one or morebus or link types, such as an industry standard architecture (ISA) bus,an extended industry standard architecture (EISA) bus, a videoelectronics standards association (VESA) bus, a peripheral componentinterconnect (PCI) bus, a peripheral component interconnect express(PCIe) bus, HyperTransport, and/or another type of bus or link. In someembodiments, there are direct connections between components. As anexample, the CPU(s) 530 may be directly connected to the main memory540. Further, the CPU(s) 530 may be directly connected to the parallelprocessing system 525. Where there is direct, or point-to-pointconnection between components, the communication bus 575 may include aPCIe link to carry out the connection. In these examples, a PCI bus neednot be included in the system 565.

Although the various blocks of FIG. 5C are shown as connected via thecommunication bus 575 with lines, this is not intended to be limitingand is for clarity only. For example, in some embodiments, apresentation component, such as display device(s) 545, may be consideredan I/O component, such as input device(s) 560 (e.g., if the display is atouch screen). As another example, the CPU(s) 530 and/or parallelprocessing system 525 may include memory (e.g., the main memory 540 maybe representative of a storage device in addition to the parallelprocessing system 525, the CPUs 530, and/or other components). In otherwords, the computing device of FIG. 5C is merely illustrative.Distinction is not made between such categories as “workstation,”“server,” “laptop,” “desktop,” “tablet,” “client device,” “mobiledevice,” “hand-held device,” “game console,” “electronic control unit(ECU),” “virtual reality system,” and/or other device or system types,as all are contemplated within the scope of the computing device of FIG.5C.

The system 565 also includes a main memory 540. Control logic (software)and data are stored in the main memory 540 which may take the form of avariety of computer-readable media. The computer-readable media may beany available media that may be accessed by the system 565. Thecomputer-readable media may include both volatile and nonvolatile media,and removable and non-removable media. By way of example, and notlimitation, the computer-readable media may comprise computer-storagemedia and communication media.

The computer-storage media may include both volatile and nonvolatilemedia and/or removable and non-removable media implemented in any methodor technology for storage of information such as computer-readableinstructions, data structures, program modules, and/or other data types.For example, the main memory 540 may store computer-readableinstructions (e.g., that represent a program(s) and/or a programelement(s), such as an operating system. Computer-storage media mayinclude, but is not limited to, RAM, ROM, EEPROM, flash memory or othermemory technology, CD-ROM, digital versatile disks (DVD) or otheroptical disk storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other medium which maybe used to store the desired information and which may be accessed bysystem 565. As used herein, computer storage media does not comprisesignals per se.

The computer storage media may embody computer-readable instructions,data structures, program modules, and/or other data types in a modulateddata signal such as a carrier wave or other transport mechanism andincludes any information delivery media. The term “modulated datasignal” may refer to a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, and not limitation, the computerstorage media may include wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared and other wireless media. Combinations of any of the aboveshould also be included within the scope of computer-readable media.

Computer programs, when executed, enable the system 565 to performvarious functions. The CPU(s) 530 may be configured to execute at leastsome of the computer-readable instructions to control one or morecomponents of the system 565 to perform one or more of the methodsand/or processes described herein. The CPU(s) 530 may each include oneor more cores (e.g., one, two, four, eight, twenty-eight, seventy-two,etc.) that are capable of handling a multitude of software threadssimultaneously. The CPU(s) 530 may include any type of processor, andmay include different types of processors depending on the type ofsystem 565 implemented (e.g., processors with fewer cores for mobiledevices and processors with more cores for servers). For example,depending on the type of system 565, the processor may be an AdvancedRISC Machines (ARM) processor implemented using Reduced Instruction SetComputing (RISC) or an x86 processor implemented using ComplexInstruction Set Computing (CISC). The system 565 may include one or moreCPUs 530 in addition to one or more microprocessors or supplementaryco-processors, such as math co-processors.

In addition to or alternatively from the CPU(s) 530, the parallelprocessing module 525 may be configured to execute at least some of thecomputer-readable instructions to control one or more components of thesystem 565 to perform one or more of the methods and/or processesdescribed herein. The parallel processing module 525 may be used by thesystem 565 to render graphics (e.g., 3D graphics) or perform generalpurpose computations. For example, the parallel processing module 525may be used for General-Purpose computing on GPUs (GPGPU). Inembodiments, the CPU(s) 530 and/or the parallel processing module 525may discretely or jointly perform any combination of the methods,processes and/or portions thereof.

The system 565 also includes input device(s) 560, the parallelprocessing system 525, and display device(s) 545. The display device(s)545 may include a display (e.g., a monitor, a touch screen, a televisionscreen, a heads-up-display (HUD), other display types, or a combinationthereof), speakers, and/or other presentation components. The displaydevice(s) 545 may receive data from other components (e.g., the parallelprocessing system 525, the CPU(s) 530, etc.), and output the data (e.g.,as an image, video, sound, etc.).

The network interface 535 may enable the system 565 to be logicallycoupled to other devices including the input devices 560, the displaydevice(s) 545, and/or other components, some of which may be built in to(e.g., integrated in) the system 565. Illustrative input devices 560include a microphone, mouse, keyboard, joystick, game pad, gamecontroller, satellite dish, scanner, printer, wireless device, etc. Theinput devices 560 may provide a natural user interface (NUI) thatprocesses air gestures, voice, or other physiological inputs generatedby a user. In some instances, inputs may be transmitted to anappropriate network element for further processing. An NUI may implementany combination of speech recognition, stylus recognition, facialrecognition, biometric recognition, gesture recognition both on screenand adjacent to the screen, air gestures, head and eye tracking, andtouch recognition (as described in more detail below) associated with adisplay of the system 565. The system 565 may be include depth cameras,such as stereoscopic camera systems, infrared camera systems, RGB camerasystems, touchscreen technology, and combinations of these, for gesturedetection and recognition. Additionally, the system 565 may includeaccelerometers or gyroscopes (e.g., as part of an inertia measurementunit (IMU)) that enable detection of motion. In some examples, theoutput of the accelerometers or gyroscopes may be used by the system 565to render immersive augmented reality or virtual reality.

Further, the system 565 may be coupled to a network (e.g., atelecommunications network, local area network (LAN), wireless network,wide area network (WAN) such as the Internet, peer-to-peer network,cable network, or the like) through a network interface 535 forcommunication purposes. The system 565 may be included within adistributed network and/or cloud computing environment.

The network interface 535 may include one or more receivers,transmitters, and/or transceivers that enable the system 565 tocommunicate with other computing devices via an electronic communicationnetwork, included wired and/or wireless communications. The networkinterface 535 may include components and functionality to enablecommunication over any of a number of different networks, such aswireless networks (e.g., Wi-Fi, Z-Wave, Bluetooth, Bluetooth LE, ZigBee,etc.), wired networks (e.g., communicating over Ethernet or InfiniBand),low-power wide-area networks (e.g., LoRaWAN, SigFox, etc.), and/or theInternet.

The system 565 may also include a secondary storage (not shown). Thesecondary storage 610 includes, for example, a hard disk drive and/or aremovable storage drive, representing a floppy disk drive, a magnetictape drive, a compact disk drive, digital versatile disk (DVD) drive,recording device, universal serial bus (USB) flash memory. The removablestorage drive reads from and/or writes to a removable storage unit in awell-known manner. The system 565 may also include a hard-wired powersupply, a battery power supply, or a combination thereof (not shown).The power supply may provide power to the system 565 to enable thecomponents of the system 565 to operate.

Each of the foregoing modules and/or devices may even be situated on asingle semiconductor platform to form the system 565. Alternately, thevarious modules may also be situated separately or in variouscombinations of semiconductor platforms per the desires of the user.

While various embodiments have been described above, it should beunderstood that they have been presented by way of example only, and notlimitation. Thus, the breadth and scope of a preferred embodiment shouldnot be limited by any of the above-described exemplary embodiments, butshould be defined only in accordance with the following claims and theirequivalents.

Machine Learning

Deep neural networks (DNNs) developed on processors, such as the PPU 400have been used for diverse use cases, from self-driving cars to fasterdrug development, from automatic image captioning in online imagedatabases to smart real-time language translation in video chatapplications. Deep learning is a technique that models the neurallearning process of the human brain, continually learning, continuallygetting smarter, and delivering more accurate results more quickly overtime. A child is initially taught by an adult to correctly identify andclassify various shapes, eventually being able to identify shapeswithout any coaching. Similarly, a deep learning or neural learningsystem needs to be trained in object recognition and classification forit get smarter and more efficient at identifying basic objects, occludedobjects, etc., while also assigning context to objects.

At the simplest level, neurons in the human brain look at various inputsthat are received, importance levels are assigned to each of theseinputs, and output is passed on to other neurons to act upon. Anartificial neuron or perceptron is the most basic model of a neuralnetwork. In one example, a perceptron may receive one or more inputsthat represent various features of an object that the perceptron isbeing trained to recognize and classify, and each of these features isassigned a certain weight based on the importance of that feature indefining the shape of an object.

A deep neural network (DNN) model includes multiple layers of manyconnected nodes (e.g., perceptrons, Boltzmann machines, radial basisfunctions, convolutional layers, etc.) that can be trained with enormousamounts of input data to quickly solve complex problems with highaccuracy. In one example, a first layer of the DNN model breaks down aninput image of an automobile into various sections and looks for basicpatterns such as lines and angles. The second layer assembles the linesto look for higher level patterns such as wheels, windshields, andmirrors. The next layer identifies the type of vehicle, and the finalfew layers generate a label for the input image, identifying the modelof a specific automobile brand.

Once the DNN is trained, the DNN can be deployed and used to identifyand classify objects or patterns in a process known as inference.Examples of inference (the process through which a DNN extracts usefulinformation from a given input) include identifying handwritten numberson checks deposited into ATM machines, identifying images of friends inphotos, delivering movie recommendations to over fifty million users,identifying and classifying different types of automobiles, pedestrians,and road hazards in driverless cars, or translating human speech inreal-time.

During training, data flows through the DNN in a forward propagationphase until a prediction is produced that indicates a labelcorresponding to the input. If the neural network does not correctlylabel the input, then errors between the correct label and the predictedlabel are analyzed, and the weights are adjusted for each feature duringa backward propagation phase until the DNN correctly labels the inputand other inputs in a training dataset. Training complex neural networksrequires massive amounts of parallel computing performance, includingfloating-point multiplications and additions that are supported by thePPU 400. Inferencing is less compute-intensive than training, being alatency-sensitive process where a trained neural network is applied tonew inputs it has not seen before to classify images, translate speech,and generally infer new information.

Neural networks rely heavily on matrix math operations, and complexmulti-layered networks require tremendous amounts of floating-pointperformance and bandwidth for both efficiency and speed. With thousandsof processing cores, optimized for matrix math operations, anddelivering tens to hundreds of TFLOPS of performance, the PPU 400 is acomputing platform capable of delivering performance required for deepneural network-based artificial intelligence and machine learningapplications.

Furthermore, images generated applying one or more of the techniquesdisclosed herein may be used to train, test, or certify DNNs used torecognize objects and environments in the real world. Such images mayinclude scenes of roadways, factories, buildings, urban settings, ruralsettings, humans, animals, and any other physical object or real-worldsetting. Such images may be used to train, test, or certify DNNs thatare employed in machines or robots to manipulate, handle, or modifyphysical objects in the real world. Furthermore, such images may be usedto train, test, or certify DNNs that are employed in autonomous vehiclesto navigate and move the vehicles through the real world. Additionally,images generated applying one or more of the techniques disclosed hereinmay be used to convey information to users of such machines, robots, andvehicles.

Graphics Processing Pipeline

In an embodiment, the PPU 400 comprises a graphics processing unit(GPU). The PPU 400 is configured to receive commands that specify shaderprograms for processing graphics data. Graphics data may be defined as aset of primitives such as points, lines, triangles, quads, trianglestrips, and the like. Typically, a primitive includes data thatspecifies a number of vertices for the primitive (e.g., in a model-spacecoordinate system) as well as attributes associated with each vertex ofthe primitive. The PPU 400 can be configured to process the graphicsprimitives to generate a frame buffer (e.g., pixel data for each of thepixels of the display).

An application writes model data for a scene (e.g., a collection ofvertices and attributes) to a memory such as a system memory or memory404. The model data defines each of the objects that may be visible on adisplay. The application then makes an API call to the driver kernelthat requests the model data to be rendered and displayed. The driverkernel reads the model data and writes commands to the one or morestreams to perform operations to process the model data. The commandsmay reference different shader programs to be implemented on theprocessing units of the PPU 400 including one or more of a vertexshader, hull shader, domain shader, geometry shader, and a pixel shader.For example, one or more of the processing units may be configured toexecute a vertex shader program that processes a number of verticesdefined by the model data. In an embodiment, the different processingunits may be configured to execute different shader programsconcurrently. For example, a first subset of processing units may beconfigured to execute a vertex shader program while a second subset ofprocessing units may be configured to execute a pixel shader program.The first subset of processing units processes vertex data to produceprocessed vertex data and writes the processed vertex data to the L2cache 460 and/or the memory 404. After the processed vertex data israsterized (e.g., transformed from three-dimensional data intotwo-dimensional data in screen space) to produce fragment data, thesecond subset of processing units executes a pixel shader to produceprocessed fragment data, which is then blended with other processedfragment data and written to the frame buffer in memory 404. The vertexshader program and pixel shader program may execute concurrently,processing different data from the same scene in a pipelined fashionuntil all of the model data for the scene has been rendered to the framebuffer. Then, the contents of the frame buffer are transmitted to adisplay controller for display on a display device.

FIG. 6A is a conceptual diagram of a graphics processing pipeline 600implemented by the PPU 400 of FIG. 4 , in accordance with an embodiment.The graphics processing pipeline 600 is an abstract flow diagram of theprocessing steps implemented to generate 2D computer-generated imagesfrom 3D geometry data. As is well-known, pipeline architectures mayperform long latency operations more efficiently by splitting up theoperation into a plurality of stages, where the output of each stage iscoupled to the input of the next successive stage. Thus, the graphicsprocessing pipeline 600 receives input data 601 that is transmitted fromone stage to the next stage of the graphics processing pipeline 600 togenerate output data 602. In an embodiment, the graphics processingpipeline 600 may represent a graphics processing pipeline defined by theOpenGL® API. As an option, the graphics processing pipeline 600 may beimplemented in the context of the functionality and architecture of theprevious Figures and/or any subsequent Figure(s).

As shown in FIG. 6A, the graphics processing pipeline 600 comprises apipeline architecture that includes a number of stages. The stagesinclude, but are not limited to, a data assembly stage 610, a vertexshading stage 620, a primitive assembly stage 630, a geometry shadingstage 640, a viewport scale, cull, and clip (VSCC) stage 650, arasterization stage 660, a fragment shading stage 670, and a rasteroperations stage 680. In an embodiment, the input data 601 comprisescommands that configure the processing units to implement the stages ofthe graphics processing pipeline 600 and geometric primitives (e.g.,points, lines, triangles, quads, triangle strips or fans, etc.) to beprocessed by the stages. The output data 602 may comprise pixel data(e.g., color data) that is copied into a frame buffer or other type ofsurface data structure in a memory.

The data assembly stage 610 receives the input data 601 that specifiesvertex data for high-order surfaces, primitives, or the like. The dataassembly stage 610 collects the vertex data in a temporary storage orqueue, such as by receiving a command from the host processor thatincludes a pointer to a buffer in memory and reading the vertex datafrom the buffer. The vertex data is then transmitted to the vertexshading stage 620 for processing.

The vertex shading stage 620 processes vertex data by performing a setof operations (e.g., a vertex shader or a program) once for each of thevertices. Vertices may be, e.g., specified as a 4-coordinate vector(e.g., <x, y, z, w>) associated with one or more vertex attributes(e.g., color, texture coordinates, surface normal, etc.). The vertexshading stage 620 may manipulate individual vertex attributes such asposition, color, texture coordinates, and the like. In other words, thevertex shading stage 620 performs operations on the vertex coordinatesor other vertex attributes associated with a vertex. Such operationscommonly including lighting operations (e.g., modifying color attributesfor a vertex) and transformation operations (e.g., modifying thecoordinate space for a vertex). For example, vertices may be specifiedusing coordinates in an object-coordinate space, which are transformedby multiplying the coordinates by a matrix that translates thecoordinates from the object-coordinate space into a world space or anormalized-device-coordinate (NCD) space. The vertex shading stage 620generates transformed vertex data that is transmitted to the primitiveassembly stage 630.

The primitive assembly stage 630 collects vertices output by the vertexshading stage 620 and groups the vertices into geometric primitives forprocessing by the geometry shading stage 640. For example, the primitiveassembly stage 630 may be configured to group every three consecutivevertices as a geometric primitive (e.g., a triangle) for transmission tothe geometry shading stage 640. In some embodiments, specific verticesmay be reused for consecutive geometric primitives (e.g., twoconsecutive triangles in a triangle strip may share two vertices). Theprimitive assembly stage 630 transmits geometric primitives (e.g., acollection of associated vertices) to the geometry shading stage 640.

The geometry shading stage 640 processes geometric primitives byperforming a set of operations (e.g., a geometry shader or program) onthe geometric primitives. Tessellation operations may generate one ormore geometric primitives from each geometric primitive. In other words,the geometry shading stage 640 may subdivide each geometric primitiveinto a finer mesh of two or more geometric primitives for processing bythe rest of the graphics processing pipeline 600. The geometry shadingstage 640 transmits geometric primitives to the viewport SCC stage 650.

In an embodiment, the graphics processing pipeline 600 may operatewithin a streaming multiprocessor and the vertex shading stage 620, theprimitive assembly stage 630, the geometry shading stage 640, thefragment shading stage 670, and/or hardware/software associatedtherewith, may sequentially perform processing operations. Once thesequential processing operations are complete, in an embodiment, theviewport SCC stage 650 may utilize the data. In an embodiment, primitivedata processed by one or more of the stages in the graphics processingpipeline 600 may be written to a cache (e.g. L1 cache, a vertex cache,etc.). In this case, in an embodiment, the viewport SCC stage 650 mayaccess the data in the cache. In an embodiment, the viewport SCC stage650 and the rasterization stage 660 are implemented as fixed functioncircuitry.

The viewport SCC stage 650 performs viewport scaling, culling, andclipping of the geometric primitives. Each surface being rendered to isassociated with an abstract camera position. The camera positionrepresents a location of a viewer looking at the scene and defines aviewing frustum that encloses the objects of the scene. The viewingfrustum may include a viewing plane, a rear plane, and four clippingplanes. Any geometric primitive entirely outside of the viewing frustummay be culled (e.g., discarded) because the geometric primitive will notcontribute to the final rendered scene. Any geometric primitive that ispartially inside the viewing frustum and partially outside the viewingfrustum may be clipped (e.g., transformed into a new geometric primitivethat is enclosed within the viewing frustum. Furthermore, geometricprimitives may each be scaled based on a depth of the viewing frustum.All potentially visible geometric primitives are then transmitted to therasterization stage 660.

The rasterization stage 660 converts the 3D geometric primitives into 2Dfragments (e.g. capable of being utilized for display, etc.). Therasterization stage 660 may be configured to utilize the vertices of thegeometric primitives to setup a set of plane equations from whichvarious attributes can be interpolated. The rasterization stage 660 mayalso compute a coverage mask for a plurality of pixels that indicateswhether one or more sample locations for the pixel intercept thegeometric primitive. In an embodiment, z-testing may also be performedto determine if the geometric primitive is occluded by other geometricprimitives that have already been rasterized. The rasterization stage660 generates fragment data (e.g., interpolated vertex attributesassociated with a particular sample location for each covered pixel)that are transmitted to the fragment shading stage 670.

The fragment shading stage 670 processes fragment data by performing aset of operations (e.g., a fragment shader or a program) on each of thefragments. The fragment shading stage 670 may generate pixel data (e.g.,color values) for the fragment such as by performing lighting operationsor sampling texture maps using interpolated texture coordinates for thefragment. The fragment shading stage 670 generates pixel data that istransmitted to the raster operations stage 680.

The raster operations stage 680 may perform various operations on thepixel data such as performing alpha tests, stencil tests, and blendingthe pixel data with other pixel data corresponding to other fragmentsassociated with the pixel. When the raster operations stage 680 hasfinished processing the pixel data (e.g., the output data 602), thepixel data may be written to a render target such as a frame buffer, acolor buffer, or the like.

It will be appreciated that one or more additional stages may beincluded in the graphics processing pipeline 600 in addition to or inlieu of one or more of the stages described above. Variousimplementations of the abstract graphics processing pipeline mayimplement different stages. Furthermore, one or more of the stagesdescribed above may be excluded from the graphics processing pipeline insome embodiments (such as the geometry shading stage 640). Other typesof graphics processing pipelines are contemplated as being within thescope of the present disclosure. Furthermore, any of the stages of thegraphics processing pipeline 600 may be implemented by one or morededicated hardware units within a graphics processor such as PPU 400.Other stages of the graphics processing pipeline 600 may be implementedby programmable hardware units such as the processing unit within thePPU 400.

The graphics processing pipeline 600 may be implemented via anapplication executed by a host processor, such as a CPU. In anembodiment, a device driver may implement an application programminginterface (API) that defines various functions that can be utilized byan application in order to generate graphical data for display. Thedevice driver is a software program that includes a plurality ofinstructions that control the operation of the PPU 400. The API providesan abstraction for a programmer that lets a programmer utilizespecialized graphics hardware, such as the PPU 400, to generate thegraphical data without requiring the programmer to utilize the specificinstruction set for the PPU 400. The application may include an API callthat is routed to the device driver for the PPU 400. The device driverinterprets the API call and performs various operations to respond tothe API call. In some instances, the device driver may performoperations by executing instructions on the CPU. In other instances, thedevice driver may perform operations, at least in part, by launchingoperations on the PPU 400 utilizing an input/output interface betweenthe CPU and the PPU 400. In an embodiment, the device driver isconfigured to implement the graphics processing pipeline 600 utilizingthe hardware of the PPU 400.

Various programs may be executed within the PPU 400 in order toimplement the various stages of the graphics processing pipeline 600.For example, the device driver may launch a kernel on the PPU 400 toperform the vertex shading stage 620 on one processing unit (or multipleprocessing units). The device driver (or the initial kernel executed bythe PPU 400) may also launch other kernels on the PPU 400 to performother stages of the graphics processing pipeline 600, such as thegeometry shading stage 640 and the fragment shading stage 670. Inaddition, some of the stages of the graphics processing pipeline 600 maybe implemented on fixed unit hardware such as a rasterizer or a dataassembler implemented within the PPU 400. It will be appreciated thatresults from one kernel may be processed by one or more interveningfixed function hardware units before being processed by a subsequentkernel on a processing unit.

Images generated applying one or more of the techniques disclosed hereinmay be displayed on a monitor or other display device. In someembodiments, the display device may be coupled directly to the system orprocessor generating or rendering the images. In other embodiments, thedisplay device may be coupled indirectly to the system or processor suchas via a network. Examples of such networks include the Internet, mobiletelecommunications networks, a WIFI network, as well as any other wiredand/or wireless networking system. When the display device is indirectlycoupled, the images generated by the system or processor may be streamedover the network to the display device. Such streaming allows, forexample, video games or other applications, which render images, to beexecuted on a server, in a data center, or in a cloud-based computingenvironment and the rendered images to be transmitted and displayed onone or more user devices (such as a computer, video game console,smartphone, other mobile device, etc.) that are physically separate fromthe server or data center. Hence, the techniques disclosed herein can beapplied to enhance the images that are streamed and to enhance servicesthat stream images such as NVIDIA GeForce Now (GFN), Google Stadia, andthe like.

Example Game Streaming System

FIG. 6B is an example system diagram for a game streaming system 605, inaccordance with some embodiments of the present disclosure. FIG. 6Bincludes game server(s) 603 (which may include similar components,features, and/or functionality to the example processing system 500 ofFIG. 5A and/or exemplary system 565 of FIG. 5B), client device(s) 604(which may include similar components, features, and/or functionality tothe example processing system 500 of FIG. 5A and/or exemplary system 565of FIG. 5B), and network(s) 606 (which may be similar to the network(s)described herein). In some embodiments of the present disclosure, thesystem 605 may be implemented.

In the system 605, for a game session, the client device(s) 604 may onlyreceive input data in response to inputs to the input device(s) 626,transmit the input data to the game server(s) 603, receive encodeddisplay data from the game server(s) 603, and display the display dataon the display 624. As such, the more computationally intense computingand processing is offloaded to the game server(s) 603 (e.g.,rendering—in particular ray or path tracing—for graphical output of thegame session is executed by the GPU(s) 615 of the game server(s) 603).In other words, the game session is streamed to the client device(s) 604from the game server(s) 603, thereby reducing the requirements of theclient device(s) 604 for graphics processing and rendering.

For example, with respect to an instantiation of a game session, aclient device 604 may be displaying a frame of the game session on thedisplay 624 based on receiving the display data from the game server(s)603. The client device 604 may receive an input to one of the inputdevice(s) 626 and generate input data in response. The client device 604may transmit the input data to the game server(s) 603 via thecommunication interface 621 and over the network(s) 606 (e.g., theInternet), and the game server(s) 603 may receive the input data via thecommunication interface 618. The CPU(s) 608 may receive the input data,process the input data, and transmit data to the GPU(s) 615 that causesthe GPU(s) 615 to generate a rendering of the game session. For example,the input data may be representative of a movement of a character of theuser in a game, firing a weapon, reloading, passing a ball, turning avehicle, etc. The rendering component 612 may render the game session(e.g., representative of the result of the input data) and the rendercapture component 614 may capture the rendering of the game session asdisplay data (e.g., as image data capturing the rendered frame of thegame session). The rendering of the game session may include ray orpath-traced lighting and/or shadow effects, computed using one or moreparallel processing units—such as GPUs 615, which may further employ theuse of one or more dedicated hardware accelerators or processing coresto perform ray or path-tracing techniques—of the game server(s) 603. Theencoder 616 may then encode the display data to generate encoded displaydata and the encoded display data may be transmitted to the clientdevice 604 over the network(s) 606 via the communication interface 618.The client device 604 may receive the encoded display data via thecommunication interface 621 and the decoder 622 may decode the encodeddisplay data to generate the display data. The client device 604 maythen display the display data via the display 624.

Example Network Environments

Network environments suitable for use in implementing embodiments of thedisclosure may include one or more client devices, servers, networkattached storage (NAS), other backend devices, and/or other devicetypes. The client devices, servers, and/or other device types (e.g.,each device) may be implemented on one or more instances of theprocessing system 500 of FIG. 5A and/or exemplary system 565 of FIG.5B—e.g., each device may include similar components, features, and/orfunctionality of the processing system 500 and/or exemplary system 565.

Components of a network environment may communicate with each other viaa network(s), which may be wired, wireless, or both. The network mayinclude multiple networks, or a network of networks. By way of example,the network may include one or more Wide Area Networks (WANs), one ormore Local Area Networks (LANs), one or more public networks such as theInternet and/or a public switched telephone network (PSTN), and/or oneor more private networks. Where the network includes a wirelesstelecommunications network, components such as a base station, acommunications tower, or even access points (as well as othercomponents) may provide wireless connectivity.

Compatible network environments may include one or more peer-to-peernetwork environments—in which case a server may not be included in anetwork environment—and one or more client-server networkenvironments—in which case one or more servers may be included in anetwork environment. In peer-to-peer network environments, functionalitydescribed herein with respect to a server(s) may be implemented on anynumber of client devices.

In at least one embodiment, a network environment may include one ormore cloud-based network environments, a distributed computingenvironment, a combination thereof, etc. A cloud-based networkenvironment may include a framework layer, a job scheduler, a resourcemanager, and a distributed file system implemented on one or more ofservers, which may include one or more core network servers and/or edgeservers. A framework layer may include a framework to support softwareof a software layer and/or one or more application(s) of an applicationlayer. The software or application(s) may respectively include web-basedservice software or applications. In embodiments, one or more of theclient devices may use the web-based service software or applications(e.g., by accessing the service software and/or applications via one ormore application programming interfaces (APIs)). The framework layer maybe, but is not limited to, a type of free and open-source software webapplication framework such as that may use a distributed file system forlarge-scale data processing (e.g., “big data”).

A cloud-based network environment may provide cloud computing and/orcloud storage that carries out any combination of computing and/or datastorage functions described herein (or one or more portions thereof).Any of these various functions may be distributed over multiplelocations from central or core servers (e.g., of one or more datacenters that may be distributed across a state, a region, a country, theglobe, etc.). If a connection to a user (e.g., a client device) isrelatively close to an edge server(s), a core server(s) may designate atleast a portion of the functionality to the edge server(s). Acloud-based network environment may be private (e.g., limited to asingle organization), may be public (e.g., available to manyorganizations), and/or a combination thereof (e.g., a hybrid cloudenvironment).

The client device(s) may include at least some of the components,features, and functionality of the example processing system 500 of FIG.5B and/or exemplary system 565 of FIG. 5C. By way of example and notlimitation, a client device may be embodied as a Personal Computer (PC),a laptop computer, a mobile device, a smartphone, a tablet computer, asmart watch, a wearable computer, a Personal Digital Assistant (PDA), anMP3 player, a virtual reality headset, a Global Positioning System (GPS)or device, a video player, a video camera, a surveillance device orsystem, a vehicle, a boat, a flying vessel, a virtual machine, a drone,a robot, a handheld communications device, a hospital device, a gamingdevice or system, an entertainment system, a vehicle computer system, anembedded system controller, a remote control, an appliance, a consumerelectronic device, a workstation, an edge device, any combination ofthese delineated devices, or any other suitable device.

It is noted that the techniques described herein may be embodied inexecutable instructions stored in a computer readable medium for use byor in connection with a processor-based instruction execution machine,system, apparatus, or device. It will be appreciated by those skilled inthe art that, for some embodiments, various types of computer-readablemedia can be included for storing data. As used herein, a“computer-readable medium” includes one or more of any suitable mediafor storing the executable instructions of a computer program such thatthe instruction execution machine, system, apparatus, or device may read(or fetch) the instructions from the computer-readable medium andexecute the instructions for carrying out the described embodiments.Suitable storage formats include one or more of an electronic, magnetic,optical, and electromagnetic format. A non-exhaustive list ofconventional exemplary computer-readable medium includes: a portablecomputer diskette; a random-access memory (RAM); a read-only memory(ROM); an erasable programmable read only memory (EPROM); a flash memorydevice; and optical storage devices, including a portable compact disc(CD), a portable digital video disc (DVD), and the like.

It should be understood that the arrangement of components illustratedin the attached Figures are for illustrative purposes and that otherarrangements are possible. For example, one or more of the elementsdescribed herein may be realized, in whole or in part, as an electronichardware component. Other elements may be implemented in software,hardware, or a combination of software and hardware. Moreover, some orall of these other elements may be combined, some may be omittedaltogether, and additional components may be added while still achievingthe functionality described herein. Thus, the subject matter describedherein may be embodied in many different variations, and all suchvariations are contemplated to be within the scope of the claims.

To facilitate an understanding of the subject matter described herein,many aspects are described in terms of sequences of actions. It will berecognized by those skilled in the art that the various actions may beperformed by specialized circuits or circuitry, by program instructionsbeing executed by one or more processors, or by a combination of both.The description herein of any sequence of actions is not intended toimply that the specific order described for performing that sequencemust be followed. All methods described herein may be performed in anysuitable order unless otherwise indicated herein or otherwise clearlycontradicted by context.

The use of the terms “a” and “an” and “the” and similar references inthe context of describing the subject matter (particularly in thecontext of the following claims) are to be construed to cover both thesingular and the plural, unless otherwise indicated herein or clearlycontradicted by context. The use of the term “at least one” followed bya list of one or more items (for example, “at least one of A and B”) isto be construed to mean one item selected from the listed items (A or B)or any combination of two or more of the listed items (A and B), unlessotherwise indicated herein or clearly contradicted by context.Furthermore, the foregoing description is for the purpose ofillustration only, and not for the purpose of limitation, as the scopeof protection sought is defined by the claims as set forth hereinaftertogether with any equivalents thereof. The use of any and all examples,or exemplary language (e.g., “such as”) provided herein, is intendedmerely to better illustrate the subject matter and does not pose alimitation on the scope of the subject matter unless otherwise claimed.The use of the term “based on” and other like phrases indicating acondition for bringing about a result, both in the claims and in thewritten description, is not intended to foreclose any other conditionsthat bring about that result. No language in the specification should beconstrued as indicating any non-claimed element as essential to thepractice of the invention as claimed.

What is claimed is:
 1. A computer-implemented method of constructing athree-dimensional (3D) representation of an object, comprising:extracting, by an encoder portion of a neural network model, featuresfrom a video including images of the object captured from a camera pose;predicting, by the neural network model, a 3D shape representation ofthe object for a first image of the images based on a set of learnedshape bases and the features; predicting, by a texture decoder portionof the neural network model, a texture flow for the first image based onthe features extracted from the first image; and mapping pixels from thefirst image to a texture space according to the texture flow to producea texture image that is consistent in the texture space with anadditional texture image produced by mapping pixels from an additionalimage of the images that is captured from a different camera pose to thetexture space, wherein transfer of the texture image according to apredefined mapping function from the texture space to the 3D shaperepresentation constructs a 3D object corresponding to the object in thefirst image.
 2. The computer-implemented method of claim 1, furthercomprising: predicting non-rigid motion deformations of the 3D shaperepresentation for the first image; and applying the non-rigid motiondeformations to an identity shape to produce the 3D shaperepresentation.
 3. The computer-implemented method of claim 1, furthercomprising: predicting non-rigid motion deformations for the images;applying the non-rigid motion deformations to identity shapes predictedfor the images to produce 3D shape representations of the object; andevaluating a loss function based on rotated differences between theidentity shapes and differences between the 3D shape representations. 4.The computer-implemented method of claim 1, further comprising:predicting, by the neural network model, an additional 3D shaperepresentation of the object for the additional image; evaluating a lossfunction based on swapping the texture image and the additional textureimage to combine the additional texture image with the 3D shaperepresentation and combine the texture image with the additional 3Dshape representation; and updating parameters of the neural networkmodel based on the loss function to encourage consistency between thetexture image and the additional texture image.
 5. Thecomputer-implemented method of claim 1, wherein the identity shape iscomputed as a sum of component shapes included in the set of learnedshape bases and each component shape is corresponding scaled by acoefficient generated by the neural network model.
 6. Thecomputer-implemented method of claim 1, wherein the 3D shaperepresentation is a mesh of vertices that define faces.
 7. Thecomputer-implemented method of claim 1, wherein the images in the videoare unlabeled.
 8. The computer-implemented method of claim 1, whereinthe neural network model is further configured to predict the camerapose.
 9. The computer-implemented method of claim 1, further comprising:transferring the texture image onto the 3D shape representation toconstruct the 3D object; projecting the 3D object according to thecamera pose to produce a rendered image; and updating parameters of theneural network model to reduce differences between the rendered imageand the first image.
 10. The computer-implemented method of claim 1,further comprising: propagating a part map across the object in a numberof the images to produce propagated part maps; mapping the propagatedpart maps into the texture space according to corresponding textureflows predicted for the number of images to produce part maps in thetexture space; and aggregating the part maps to produce a video-levelpart map.
 11. The computer-implemented method of claim 10, furthercomprising: rendering 3D shape representations predicted for the numberof the images according to associated camera poses, wherein thevideo-level part map is transferred onto each one of the 3D shaperepresentations to produce rendered images; and updating parameters ofthe neural network model to encourage consistency between the renderedimages and the propagated part maps.
 12. The computer-implemented methodof claim 10, propagating the part map comprises applying a part patternto the object in a middle image that is centered within the number ofimages and propagating the part pattern from the middle image to imagesbefore and after the center image in the video.
 13. Thecomputer-implemented method of claim 1, wherein the images are eachannotated and further comprising: mapping the annotations into thetexture space according to corresponding texture flows predicted for theimages to produce annotation maps in the texture space; and aggregatingthe annotation maps to produce a canonical annotation map for the video.14. The computer-implemented method of claim 13, further comprising:transferring the canonical annotation map to 3D shape representationspredicted for the images to produce annotated 3D shape representations;projecting the annotated 3D shape representations according toassociated camera poses, to produce projected annotations for theimages; and updating parameters of the neural network model to encourageconsistency between the projected annotations and the annotations. 15.The computer-implemented method of claim 13, wherein at least one of theannotations is a semantic keypoint.
 16. The computer-implemented methodof claim 1, wherein the object is non-rigid animal.
 17. Thecomputer-implemented method of claim 1, wherein the steps of extracting,predicting the 3D shape representation, predicting the texture flow, andmapping are performed on a server in a data center, or in a cloud-basedcomputing environment to construct the 3D object, and the 3D object isstreamed to a user device.
 18. The computer-implemented method of claim1, wherein the steps of extracting, predicting the 3D shaperepresentation, predicting the texture flow, and mapping are performedto generate the 3D object that is used for training, testing, orcertifying a second neural network that is employed in a machine, robot,or autonomous vehicle.
 19. A system, comprising: a neural network modelconfigured to construct a three-dimensional (3D) representation of anobject by: extracting, by an encoder portion of the neural networkmodel, features from a video including images of the object capturedfrom a camera pose; predicting a 3D shape representation of the objectfor a first image of the images based on a set of learned shape basesand the features; predicting, by a texture decoder portion of the neuralnetwork model, a texture flow for the first image based on the featuresextracted from the first image; and mapping pixels from the first imageto a texture space according to the texture flow to produce a textureimage that is consistent in the texture space with an additional textureimage produced by mapping pixels from an additional image of the imagesthat is captured from a different camera pose to the texture space,wherein transfer of the texture image according to a predefined mappingfunction from the texture space to the 3D shape representationconstructs a 3D object corresponding to the object in the first image.20. A non-transitory computer-readable media storing computerinstructions for constructing a three-dimensional (3D) representation ofan object that, when executed by one or more processors, cause the oneor more processors to perform the steps of: extracting, by an encoderportion of a neural network model, features from a video includingimages of the object captured from a camera pose; predicting, by theneural network model, a 3D shape representation of the object for afirst image of the images based on a set of learned shape bases and thefeatures; predicting, by a texture decoder portion of the neural networkmodel, a texture flow for the first image based on the featuresextracted from the first image; and mapping pixels from the first imageto a texture space according to the texture flow to produce a textureimage that is consistent in the texture space with an additional textureimage produced by mapping pixels from an additional image of the imagesthat is captured from a different camera pose to the texture space,wherein transfer of the texture image according to a predefined mappingfunction from the texture space to the 3D shape representationconstructs a 3D object corresponding to the object in the first image.